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Essential Review Papers on Physics-Informed Neural Networks: A Curated Guide for Practitioners

Staying on top of a fast-growing research field is never easy. I face this challenge firsthand as a practitioner in Physics-Informed Neural Networks (PINNs). New papers, be they algorithmic advancements or cutting-edge applications, are published at an accelerating pace by both academia and industry. While it is exciting to see this rapid development, it inevitably raises a pressing question: How can one stay informed without spending countless hours sifting through papers? This is where I have found review papers to be exceptionally valuable. Good review papers are effective tools that distill essential insights and highlight important trends. They are big-time savers guiding us through the flood of information. In this blog post, I would like to share with you my personal, curated list of must-read review papers on PINNs, that are especially influential for my own understanding and use of PINNs. Those papers cover key aspects of PINNs, including algorithmic developments, implementation best practices, and real-world applications. In addition to what’s available in existing literature, I’ve included one of my own review papers, which provides a comprehensive analysis of common functional usage patterns of PINNs — a practical perspective often missing from academic reviews. This analysis is based on my review of around 200 arXiv papers on PINNs across various engineering domains in the past 3 years and can serve as an essential guide for practitioners looking to deploy these techniques to tackle real-world challenges. For each review paper, I will explain why it deserves your attention by explaining its unique perspective and indicating practical takeaways that you can benefit from immediately. Whether you’re just getting started with PINNs, using them to tackle real-world problems, or exploring new research directions, I hope this collection makes navigating the busy field of PINN research easier for you. Let’s cut through the complexity together and focus on what truly matters. 1️⃣ Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and what’s next 📄 Paper at a glance 🔍 What it covers Authors: S. Cuomo, V. Schiano di Cola, F. Giampaolo, G. Rozza, M. Raissi, and F. Piccialli Year: 2022 Link: arXiv This review is structured around key themes in PINNs: the fundamental components that define their architecture, theoretical aspects of their learning process, and their application to various computing challenges in engineering. The paper also explores the available toolsets, emerging trends, and future directions. Fig 1. Overview of the #1 review paper. (Image by author) ✨ What’s unique This review paper stands out in the following ways: One of the best introductions to PINN fundamentals. This paper takes a well-paced approach to explaining PINNs from the ground up. Section 2 systematically dissects the building blocks of a PINN, covering various underlying neural network architectures and their associated characteristics, how PDE constraints are incorporated, common training methodologies, and learning theory (convergence, error analysis, etc.) of PINNs. Putting PINNs in historical context. Rather than simply presenting PINNs as a standalone solution, the paper traces their development from earlier work on using deep learning to solve differential equations. This historical framing is valuable because it helps demystify PINNs by showing that they are an evolution of previous ideas, and it makes it easier for practitioners to see what alternatives are available. Equation-driven organization. Instead of just classifying PINN research by scientific domains (e.g., geoscience, material science, etc.) as many other reviews do, this paper categorizes PINNs based on the types of differential equations (e.g., diffusion problems, advection problems, etc.) they solve. This equation-first perspective encourages knowledge transfer as the same set of PDEs could be used across multiple scientific domains. In addition, it makes it easier for practitioners to see the strengths and weaknesses of PINNs when dealing with different types of differential equations. 🛠 Practical goodies Beyond its theoretical insights, this review paper offers immediately useful resources for practitioners: A complete implementation example. In section 3.4, this paper walks through a full PINN implementation to solve a 1D Nonlinear Schrödinger equation. It covers translating equations into PINN formulations, handling boundary and initial conditions, defining neural network architectures, choosing training strategies, selecting collocation points, and applying optimization methods. All implementation details are clearly documented for easy reproducibility. The paper compares PINN performance by varying different hyperparameters, which could offer immediately applicable insights for your own PINN experiments. Available frameworks and software tools. Table 3 compiles a comprehensive list of major PINN toolkits, with detailed tool descriptions provided in section 4.3. The considered backends include not only Tensorflow and PyTorch but also Julia and Jax. This side-by-side comparison of different frameworks is especially useful for picking the right tool for your needs. 💡Who would benefit This review paper benefits anyone new to PINNs and looking for a clear, structured introduction. Engineers and developers looking for practical implementation guidance would find the realistic, hands-on demo, and the thorough comparison of existing PINN frameworks most interesting. Additionally, they can find relevant prior work on differential equations similar to their current problem, which offers insights they can leverage in their own problem-solving. Researchers investigating theoretical aspects of PINN convergence, optimization, or efficiency can also greatly benefit from this paper. 2️⃣ From PINNs to PIKANs: Recent Advances in Physics-Informed Machine Learning 📄 Paper at a glance Authors: J. D. Toscano, V. Oommen, A. J. Varghese, Z. Zou, N. A. Daryakenari, C. Wu, and G. E. Karniadakis Year: 2024 Link: arXiv 🔍 What it covers This paper provides one of the most up-to-date overviews of the latest advancements in PINNs. It emphasises enhancements in network design, feature expansion, optimization strategies, uncertainty quantification, and theoretical insights. The paper also surveys key applications across a range of domains. Fig 2. Overview of the #2 review paper. (Image by author) ✨ What’s unique This review paper stands out in the following ways: A structured taxonomy of algorithmic developments. One of the most fresh contributions of this paper is its taxonomy of algorithmic advancements. This new taxonomy scheme elegantly categorizes all the advancements into three core areas: (1) representation model, (2) handling governing equations, and (3) optimization process. This structure provides a clear framework for understanding both current developments and potential directions for future research. In addition, the illustrations used in the paper are top-notch and easily digestible. Fig 3. The taxonomy of algorithmic developments in PINNs proposed by the #2 paper. (Image by author) Spotlight on Physics-informed Kolmogorov–Arnold Networks (KAN). KAN, a new architecture based on the Kolmogorov–Arnold representation theorem, is currently a hot topic in deep learning. In the PINN community, some work has already been done to replace the multilayer perceptions (MLP) representation with KANs to gain more expressiveness and training efficiency. The community lacks a comprehensive review of this new line of research. This review paper (section 3.1) exactly fills in the gap. Review on uncertainty quantification (UQ) in PINNs. UQ is essential for the reliable and trustworthy deployment of PINNs when tackling real-world engineering applications. In section 5, this paper provides a dedicated section on UQ, explaining the common sources of uncertainty in solving differential equations with PINNs and reviewing strategies for quantifying prediction confidence. Theoretical advances in PINN training dynamics. In practice, training PINNs is non-trivial. Practitioners are often puzzled by why PINNs training sometimes fail, or how they should be trained optimally. In section 6.2, this paper provides one of the most detailed and up-to-date discussions on this aspect, covering the Neural Tangent Kernel (NTK) analysis of PINNs, information bottleneck theory, and multi-objective optimization challenges. 🛠 Practical goodies Even though this review paper leans towards the theory-heavy side, two particularly valuable aspects stand out from a practical perspective: A timeline of algorithmic advances in PINNs. In Appendix A Table, this paper tracks the milestones of key advancements in PINNs, from the original PINN formulation to the most recent extensions to KANs. If you’re working on algorithmic improvements, this timeline gives you a clear view of what’s already been done. If you’re struggling with PINN training or accuracy, you can use this table to find existing methods that might solve your issue. A broad overview of PINN applications across domains. Compared to all the other reviews, this paper strives to give the most comprehensive and updated coverage of PINN applications in not only the engineering domains but also other less-covered fields such as finance. Practitioners can easily find prior works conducted in their domains and draw inspiration. 💡Who would benefit For practitioners working in safety-critical fields that need confidence intervals or reliability estimates on their PINN predictions, the discussion on UQ would be useful. If you are struggling with PINN training instability, slow convergence, or unexpected failures, the discussion on PINN training dynamics can help unpack the theoretical reasons behind these issues. Researchers may find this paper especially interesting because of the new taxonomy, which allows them to see patterns and identify gaps and opportunities for novel contributions. In addition, the review of cutting-edge work on PI-KAN can also be inspiring. 3️⃣ Physics-Informed Neural Networks: An Application-Centric Guide 📄 Paper at a glance Authors: S. Guo (this author) Year: 2024 Link: Medium 🔍 What it covers This article reviews how PINNs are used to tackle different types of engineering tasks. For each task category, the article discusses the problem statement, why PINNs are useful, how PINNs can be implemented to address the problem, and is followed by a concrete use case published in the literature. Fig 4. Overview of the #3 review paper. (Image by author) ✨ What’s unique Unlike most reviews that categorize PINN applications either based on the type of differential equations solved or specific engineering domains, this article picks an angle that practitioners care about the most: the engineering tasks solved by PINNs. This work is based on reviewing papers on PINN case studies scattered in various engineering domains. The outcome is a list of distilled recurring functional usage patterns of PINNs: Predictive modeling and simulations, where PINNs are leveraged for dynamical system forecasting, coupled system modeling, and surrogate modeling. Optimization, where PINNs are commonly employed to achieve efficient design optimization, inverse design, model predictive control, and optimized sensor placement. Data-driven insights, where PINNs are used to identify the unknown parameters or functional forms of the system, as well as to assimilate observational data to better estimate the system states. Data-driven enhancement, where PINNs are used to reconstruct the field and enhance the resolution of the observational data. Monitoring, diagnostic, and health assessment, where PINNs are leveraged to act as virtual sensors, anomaly detectors, health monitors, and predictive maintainers. 🛠 Practical goodies This article places practitioners’ needs at the forefront. While most existing review papers merely answer the question, “Has PINN been used in my field?”, practitioners often seek more specific guidance: “Has PINN been used for the type of problem I’m trying to solve?”. This is precisely what this article tries to address. By using the proposed five-category functional classification, practitioners can conveniently map their problems to these categories, see how others have solved them, and what worked and what did not. Instead of reinventing the wheel, practitioners can leverage established use cases and adapt proven solutions to their own problems. 💡Who would benefit This review is best for practitioners who want to see how PINNs are actually being used in the real world. It can also be particularly valuable for cross-disciplinary innovation, as practitioners can learn from solutions developed in other fields. 4️⃣ An Expert’s Guide to Training Physics-informed Neural Networks 📄 Paper at a glance Authors: S. Wang, S. Sankaran, H. Wang, P. Perdikaris Year: 2023 Link: arXiv 🔍 What it covers Even though it doesn’t market itself as a “standard” review, this paper goes all in on providing a comprehensive handbook for training PINNs. It presents a detailed set of best practices for training physics-informed neural networks (PINNs), addressing issues like spectral bias, unbalanced loss terms, and causality violations. It also introduces challenging benchmarks and extensive ablation studies to demonstrate these methods. Fig 5. Overview of the #4 review paper. (Image by author) ✨ What’s unique A unified “expert’s guide”. The main authors are active researchers in PINNs, working extensively on improving PINN training efficiency and model accuracy for the past years. This paper is a distilled summary of the authors’ past work, synthesizing a broad range of recent PINN techniques (e.g., Fourier feature embeddings, adaptive loss weighting, causal training) into a cohesive training pipeline. This feels like having a mentor who tells you exactly what does and doesn’t work with PINNs. A thorough hyperparameter tuning study. This paper conducts various experiments to show how different tweaks (e.g., different architectures, training schemes, etc.) play out on different PDE tasks. Their ablation studies show precisely which methods move the needle, and by how much. PDE benchmarks. The paper compiles a suite of challenging PDE benchmarks and offers state-of-the-art results that PINNs can achieve. 🛠 Practical goodies A problem-solution cheat sheet. This paper thoroughly documents various techniques addressing common PINN training pain-points. Each technique is clearly presented using a structured format: the why (motivation), how (how the approach addresses the problem), and what (the implementation details). This makes it very easy for practitioners to identify the “cure” based on the “symptoms” observed in their PINN training process. What’s great is that the authors transparently discussed potential pitfalls of each approach, allowing practitioners to make well-informed decisions and effective trade-offs. Empirical insights. The paper shares valuable empirical insights obtained from extensive hyperparameter tuning experiments. It offers practical guidance on choosing suitable hyperparameters, e.g., network architectures and learning rate schedules, and demonstrates how these parameters interact with the advanced PINN training techniques proposed. Ready-to-use library. The paper is accompanied by an optimized JAX library that practitioners can directly adopt or customize. The library supports multi-GPU environments and is ready for scaling to large-scale problems. 💡Who would benefit Practitioners who are struggling with unstable or slow PINN training can find many practical strategies to fix common pathologies. They can also benefit from the straightforward templates (in JAX) to quickly adapt PINNs to their own PDE setups. Researchers looking for challenging benchmark problems and aiming to benchmark new PINN ideas against well-documented baselines will find this paper especially handy. 5️⃣ Domain-Specific Review Papers Beyond general reviews in PINNs, there are several nice review papers that focus on specific scientific and engineering domains. If you’re working in one of these fields, these reviews could provide a deeper dive into best practices and cutting-edge applications. 1. Heat Transfer Problems Paper: Physics-Informed Neural Networks for Heat Transfer Problems The paper provides an application-centric discussion on how PINNs can be used to tackle various thermal engineering problems, including inverse heat transfer, convection-dominated flows, and phase-change modeling. It highlights real-world challenges such as missing boundary conditions, sensor-driven inverse problems, and adaptive cooling system design. The industrial case study related to power electronics is particularly insightful for understanding the usage of PINNs in practice. 2. Power Systems Paper: Applications of Physics-Informed Neural Networks in Power Systems — A Review This paper offers a structured overview of how PINNs are applied to critical power grid challenges, including state/parameter estimation, dynamic analysis, power flow calculation, optimal power flow (OPF), anomaly detection, and model synthesis. For each type of application, the paper discusses the shortcomings of traditional power system solutions and explains why PINNs could be advantageous in addressing those shortcomings. This comparative summary is useful for understanding the motivation for adopting PINNs. 3. Fluid Mechanics Paper: Physics-informed neural networks (PINNs) for fluid mechanics: A review This paper explored three detailed case studies that demonstrate PINNs application in fluid dynamics: (1) 3D wake flow reconstruction using sparse 2D velocity data, (2) inverse problems in compressible flow (e.g., shock wave prediction with minimal boundary data), and (3) biomedical flow modeling, where PINNs infer thrombus material properties from phase-field data. The paper highlights how PINNs overcome limitations in traditional CFD, e.g., mesh dependency, expensive data assimilation, and difficulty handling ill-posed inverse problems. 4. Additive Manufacturing Paper: A review on physics-informed machine learning for monitoring metal additive manufacturing process This paper examines how PINNs address critical challenges specific to additive manufacturing process prediction or monitoring, including temperature field prediction, fluid dynamics modeling, fatigue life estimation, accelerated finite element simulations, and process characteristics prediction. 6️⃣ Conclusion In this blog post, we went through a curated list of review papers on PINNs, covering fundamental theoretical insights, the latest algorithmic advancements, and practical application-oriented perspectives. For each paper, we highlighted unique contributions, key takeaways, and the audience that would benefit the most from these insights. I hope this curated collection can help you better navigate the evolving field of PINNs.

Staying on top of a fast-growing research field is never easy.

I face this challenge firsthand as a practitioner in Physics-Informed Neural Networks (PINNs). New papers, be they algorithmic advancements or cutting-edge applications, are published at an accelerating pace by both academia and industry. While it is exciting to see this rapid development, it inevitably raises a pressing question:

How can one stay informed without spending countless hours sifting through papers?

This is where I have found review papers to be exceptionally valuable. Good review papers are effective tools that distill essential insights and highlight important trends. They are big-time savers guiding us through the flood of information.

In this blog post, I would like to share with you my personal, curated list of must-read review papers on PINNs, that are especially influential for my own understanding and use of PINNs. Those papers cover key aspects of PINNs, including algorithmic developments, implementation best practices, and real-world applications.

In addition to what’s available in existing literature, I’ve included one of my own review papers, which provides a comprehensive analysis of common functional usage patterns of PINNs — a practical perspective often missing from academic reviews. This analysis is based on my review of around 200 arXiv papers on PINNs across various engineering domains in the past 3 years and can serve as an essential guide for practitioners looking to deploy these techniques to tackle real-world challenges.

For each review paper, I will explain why it deserves your attention by explaining its unique perspective and indicating practical takeaways that you can benefit from immediately.

Whether you’re just getting started with PINNs, using them to tackle real-world problems, or exploring new research directions, I hope this collection makes navigating the busy field of PINN research easier for you.

Let’s cut through the complexity together and focus on what truly matters.

1️⃣ Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and what’s next

📄 Paper at a glance

🔍 What it covers

  • Authors: S. Cuomo, V. Schiano di Cola, F. Giampaolo, G. Rozza, M. Raissi, and F. Piccialli
  • Year: 2022
  • Link: arXiv

This review is structured around key themes in PINNs: the fundamental components that define their architecture, theoretical aspects of their learning process, and their application to various computing challenges in engineering. The paper also explores the available toolsets, emerging trends, and future directions.

Fig 1. Overview of the #1 review paper. (Image by author)

✨ What’s unique

This review paper stands out in the following ways:

  • One of the best introductions to PINN fundamentals. This paper takes a well-paced approach to explaining PINNs from the ground up. Section 2 systematically dissects the building blocks of a PINN, covering various underlying neural network architectures and their associated characteristics, how PDE constraints are incorporated, common training methodologies, and learning theory (convergence, error analysis, etc.) of PINNs.
  • Putting PINNs in historical context. Rather than simply presenting PINNs as a standalone solution, the paper traces their development from earlier work on using deep learning to solve differential equations. This historical framing is valuable because it helps demystify PINNs by showing that they are an evolution of previous ideas, and it makes it easier for practitioners to see what alternatives are available.
  • Equation-driven organization. Instead of just classifying PINN research by scientific domains (e.g., geoscience, material science, etc.) as many other reviews do, this paper categorizes PINNs based on the types of differential equations (e.g., diffusion problems, advection problems, etc.) they solve. This equation-first perspective encourages knowledge transfer as the same set of PDEs could be used across multiple scientific domains. In addition, it makes it easier for practitioners to see the strengths and weaknesses of PINNs when dealing with different types of differential equations.

🛠 Practical goodies

Beyond its theoretical insights, this review paper offers immediately useful resources for practitioners:

  • A complete implementation example. In section 3.4, this paper walks through a full PINN implementation to solve a 1D Nonlinear Schrödinger equation. It covers translating equations into PINN formulations, handling boundary and initial conditions, defining neural network architectures, choosing training strategies, selecting collocation points, and applying optimization methods. All implementation details are clearly documented for easy reproducibility. The paper compares PINN performance by varying different hyperparameters, which could offer immediately applicable insights for your own PINN experiments.
  • Available frameworks and software tools. Table 3 compiles a comprehensive list of major PINN toolkits, with detailed tool descriptions provided in section 4.3. The considered backends include not only Tensorflow and PyTorch but also Julia and Jax. This side-by-side comparison of different frameworks is especially useful for picking the right tool for your needs.

💡Who would benefit

  • This review paper benefits anyone new to PINNs and looking for a clear, structured introduction.
  • Engineers and developers looking for practical implementation guidance would find the realistic, hands-on demo, and the thorough comparison of existing PINN frameworks most interesting. Additionally, they can find relevant prior work on differential equations similar to their current problem, which offers insights they can leverage in their own problem-solving.
  • Researchers investigating theoretical aspects of PINN convergence, optimization, or efficiency can also greatly benefit from this paper.

2️⃣ From PINNs to PIKANs: Recent Advances in Physics-Informed Machine Learning

📄 Paper at a glance

  • Authors: J. D. Toscano, V. Oommen, A. J. Varghese, Z. Zou, N. A. Daryakenari, C. Wu, and G. E. Karniadakis
  • Year: 2024
  • Link: arXiv

🔍 What it covers

This paper provides one of the most up-to-date overviews of the latest advancements in PINNs. It emphasises enhancements in network design, feature expansion, optimization strategies, uncertainty quantification, and theoretical insights. The paper also surveys key applications across a range of domains.

Fig 2. Overview of the #2 review paper. (Image by author)

✨ What’s unique

This review paper stands out in the following ways:

  • A structured taxonomy of algorithmic developments. One of the most fresh contributions of this paper is its taxonomy of algorithmic advancements. This new taxonomy scheme elegantly categorizes all the advancements into three core areas: (1) representation model, (2) handling governing equations, and (3) optimization process. This structure provides a clear framework for understanding both current developments and potential directions for future research. In addition, the illustrations used in the paper are top-notch and easily digestible.
Fig 3. The taxonomy of algorithmic developments in PINNs proposed by the #2 paper. (Image by author)
  • Spotlight on Physics-informed Kolmogorov–Arnold Networks (KAN). KAN, a new architecture based on the Kolmogorov–Arnold representation theorem, is currently a hot topic in deep learning. In the PINN community, some work has already been done to replace the multilayer perceptions (MLP) representation with KANs to gain more expressiveness and training efficiency. The community lacks a comprehensive review of this new line of research. This review paper (section 3.1) exactly fills in the gap.
  • Review on uncertainty quantification (UQ) in PINNs. UQ is essential for the reliable and trustworthy deployment of PINNs when tackling real-world engineering applications. In section 5, this paper provides a dedicated section on UQ, explaining the common sources of uncertainty in solving differential equations with PINNs and reviewing strategies for quantifying prediction confidence.
  • Theoretical advances in PINN training dynamics. In practice, training PINNs is non-trivial. Practitioners are often puzzled by why PINNs training sometimes fail, or how they should be trained optimally. In section 6.2, this paper provides one of the most detailed and up-to-date discussions on this aspect, covering the Neural Tangent Kernel (NTK) analysis of PINNs, information bottleneck theory, and multi-objective optimization challenges.

🛠 Practical goodies

Even though this review paper leans towards the theory-heavy side, two particularly valuable aspects stand out from a practical perspective:

  • A timeline of algorithmic advances in PINNs. In Appendix A Table, this paper tracks the milestones of key advancements in PINNs, from the original PINN formulation to the most recent extensions to KANs. If you’re working on algorithmic improvements, this timeline gives you a clear view of what’s already been done. If you’re struggling with PINN training or accuracy, you can use this table to find existing methods that might solve your issue.
  • A broad overview of PINN applications across domains. Compared to all the other reviews, this paper strives to give the most comprehensive and updated coverage of PINN applications in not only the engineering domains but also other less-covered fields such as finance. Practitioners can easily find prior works conducted in their domains and draw inspiration.

💡Who would benefit

  • For practitioners working in safety-critical fields that need confidence intervals or reliability estimates on their PINN predictions, the discussion on UQ would be useful. If you are struggling with PINN training instability, slow convergence, or unexpected failures, the discussion on PINN training dynamics can help unpack the theoretical reasons behind these issues.
  • Researchers may find this paper especially interesting because of the new taxonomy, which allows them to see patterns and identify gaps and opportunities for novel contributions. In addition, the review of cutting-edge work on PI-KAN can also be inspiring.

3️⃣ Physics-Informed Neural Networks: An Application-Centric Guide

📄 Paper at a glance

  • Authors: S. Guo (this author)
  • Year: 2024
  • Link: Medium

🔍 What it covers

This article reviews how PINNs are used to tackle different types of engineering tasks. For each task category, the article discusses the problem statement, why PINNs are useful, how PINNs can be implemented to address the problem, and is followed by a concrete use case published in the literature.

Fig 4. Overview of the #3 review paper. (Image by author)

✨ What’s unique

Unlike most reviews that categorize PINN applications either based on the type of differential equations solved or specific engineering domains, this article picks an angle that practitioners care about the most: the engineering tasks solved by PINNs. This work is based on reviewing papers on PINN case studies scattered in various engineering domains. The outcome is a list of distilled recurring functional usage patterns of PINNs:

  • Predictive modeling and simulations, where PINNs are leveraged for dynamical system forecasting, coupled system modeling, and surrogate modeling.
  • Optimization, where PINNs are commonly employed to achieve efficient design optimization, inverse design, model predictive control, and optimized sensor placement.
  • Data-driven insights, where PINNs are used to identify the unknown parameters or functional forms of the system, as well as to assimilate observational data to better estimate the system states.
  • Data-driven enhancement, where PINNs are used to reconstruct the field and enhance the resolution of the observational data.
  • Monitoring, diagnostic, and health assessment, where PINNs are leveraged to act as virtual sensors, anomaly detectors, health monitors, and predictive maintainers.

🛠 Practical goodies

This article places practitioners’ needs at the forefront. While most existing review papers merely answer the question, “Has PINN been used in my field?”, practitioners often seek more specific guidance: “Has PINN been used for the type of problem I’m trying to solve?”. This is precisely what this article tries to address.

By using the proposed five-category functional classification, practitioners can conveniently map their problems to these categories, see how others have solved them, and what worked and what did not. Instead of reinventing the wheel, practitioners can leverage established use cases and adapt proven solutions to their own problems.

💡Who would benefit

This review is best for practitioners who want to see how PINNs are actually being used in the real world. It can also be particularly valuable for cross-disciplinary innovation, as practitioners can learn from solutions developed in other fields.

4️⃣ An Expert’s Guide to Training Physics-informed Neural Networks

📄 Paper at a glance

  • Authors: S. Wang, S. Sankaran, H. Wang, P. Perdikaris
  • Year: 2023
  • Link: arXiv

🔍 What it covers

Even though it doesn’t market itself as a “standard” review, this paper goes all in on providing a comprehensive handbook for training PINNs. It presents a detailed set of best practices for training physics-informed neural networks (PINNs), addressing issues like spectral bias, unbalanced loss terms, and causality violations. It also introduces challenging benchmarks and extensive ablation studies to demonstrate these methods.

Fig 5. Overview of the #4 review paper. (Image by author)

✨ What’s unique

  • A unified “expert’s guide”. The main authors are active researchers in PINNs, working extensively on improving PINN training efficiency and model accuracy for the past years. This paper is a distilled summary of the authors’ past work, synthesizing a broad range of recent PINN techniques (e.g., Fourier feature embeddings, adaptive loss weighting, causal training) into a cohesive training pipeline. This feels like having a mentor who tells you exactly what does and doesn’t work with PINNs.
  • A thorough hyperparameter tuning study. This paper conducts various experiments to show how different tweaks (e.g., different architectures, training schemes, etc.) play out on different PDE tasks. Their ablation studies show precisely which methods move the needle, and by how much.
  • PDE benchmarks. The paper compiles a suite of challenging PDE benchmarks and offers state-of-the-art results that PINNs can achieve.

🛠 Practical goodies

  • A problem-solution cheat sheet. This paper thoroughly documents various techniques addressing common PINN training pain-points. Each technique is clearly presented using a structured format: the why (motivation), how (how the approach addresses the problem), and what (the implementation details). This makes it very easy for practitioners to identify the “cure” based on the “symptoms” observed in their PINN training process. What’s great is that the authors transparently discussed potential pitfalls of each approach, allowing practitioners to make well-informed decisions and effective trade-offs.
  • Empirical insights. The paper shares valuable empirical insights obtained from extensive hyperparameter tuning experiments. It offers practical guidance on choosing suitable hyperparameters, e.g., network architectures and learning rate schedules, and demonstrates how these parameters interact with the advanced PINN training techniques proposed.
  • Ready-to-use library. The paper is accompanied by an optimized JAX library that practitioners can directly adopt or customize. The library supports multi-GPU environments and is ready for scaling to large-scale problems.

💡Who would benefit

  • Practitioners who are struggling with unstable or slow PINN training can find many practical strategies to fix common pathologies. They can also benefit from the straightforward templates (in JAX) to quickly adapt PINNs to their own PDE setups.
  • Researchers looking for challenging benchmark problems and aiming to benchmark new PINN ideas against well-documented baselines will find this paper especially handy.

5️⃣ Domain-Specific Review Papers

Beyond general reviews in PINNs, there are several nice review papers that focus on specific scientific and engineering domains. If you’re working in one of these fields, these reviews could provide a deeper dive into best practices and cutting-edge applications.

1. Heat Transfer Problems

Paper: Physics-Informed Neural Networks for Heat Transfer Problems

The paper provides an application-centric discussion on how PINNs can be used to tackle various thermal engineering problems, including inverse heat transfer, convection-dominated flows, and phase-change modeling. It highlights real-world challenges such as missing boundary conditions, sensor-driven inverse problems, and adaptive cooling system design. The industrial case study related to power electronics is particularly insightful for understanding the usage of PINNs in practice.

2. Power Systems

Paper: Applications of Physics-Informed Neural Networks in Power Systems — A Review

This paper offers a structured overview of how PINNs are applied to critical power grid challenges, including state/parameter estimation, dynamic analysis, power flow calculation, optimal power flow (OPF), anomaly detection, and model synthesis. For each type of application, the paper discusses the shortcomings of traditional power system solutions and explains why PINNs could be advantageous in addressing those shortcomings. This comparative summary is useful for understanding the motivation for adopting PINNs.

3. Fluid Mechanics

Paper: Physics-informed neural networks (PINNs) for fluid mechanics: A review

This paper explored three detailed case studies that demonstrate PINNs application in fluid dynamics: (1) 3D wake flow reconstruction using sparse 2D velocity data, (2) inverse problems in compressible flow (e.g., shock wave prediction with minimal boundary data), and (3) biomedical flow modeling, where PINNs infer thrombus material properties from phase-field data. The paper highlights how PINNs overcome limitations in traditional CFD, e.g., mesh dependency, expensive data assimilation, and difficulty handling ill-posed inverse problems.

4. Additive Manufacturing

Paper: A review on physics-informed machine learning for monitoring metal additive manufacturing process

This paper examines how PINNs address critical challenges specific to additive manufacturing process prediction or monitoring, including temperature field prediction, fluid dynamics modeling, fatigue life estimation, accelerated finite element simulations, and process characteristics prediction.

6️⃣ Conclusion

In this blog post, we went through a curated list of review papers on PINNs, covering fundamental theoretical insights, the latest algorithmic advancements, and practical application-oriented perspectives. For each paper, we highlighted unique contributions, key takeaways, and the audience that would benefit the most from these insights. I hope this curated collection can help you better navigate the evolving field of PINNs.

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Business leaders and SNP call on Starmer to visit Aberdeen amid North Sea job losses

Aberdeen business leaders and the SNP are calling on the Prime Minister to visit the north-east of Scotland as they blamed Labour policies for yet more job losses in the oil and gas sector. On Wednesday, Harbour Energy announced that it would cut 250 jobs from its onshore operations, accounting for a 25% reduction in headcount. The UK’s largest producer of oil and gas has claimed that the hostile fiscal policy facing oil and gas businesses prompted the decision as it slows investment in the country, opting to allocate funds overseas. On the day of this announcement, Aberdeen South MP and SNP Westminster leader Stephen Flynn brought the news to the attention of prime minister Sir Keir Starmer. © BloombergEmissions from chimneys at the British Steel Ltd. plant in Scunthorpe, UK. He asked Starmer to “explain to my constituents why he is willing to move heaven and earth to save jobs in Scunthorpe while destroying jobs in Scotland.” The SNP leader was referring to the government’s recent move to nationalise British Steel. The UK government took control of the British steel company from its Chinese owner, Jingye Group, after losses from its steelmaking operations forced it to the brink. Now the SNP MP, alongside his colleagues in Westminster and Holyrood, has written to the Labour Party leader, inviting him to see the impacts his government’s energy policy is having on Aberdeen and its people. “We are writing to you as the local MPs and MSPs for Aberdeen, to invite you to urgently visit Aberdeen to meet with local representatives, businesses, trade unions and workers to hear about the damaging impact that Labour government policies are having on Scottish energy jobs – and to discuss the urgent investment needed to protect jobs and deliver prosperity,” the letter reads. ‘Haemorrhaging investment in

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Oil Gains 3% as Trade Hopes Rise

Oil rose as President Donald Trump announced a trade framework with the UK, spurring some optimism about deals to come. West Texas Intermediate climbed 3.2% to approach $60 a barrel. Trump said the UK would fast-track US items through its customs process and reduce barriers on billions of dollars of agricultural, chemical, energy and industrial exports, including ethanol. Notably, the terms are limited in scope and a 10% baseline tariff remains. The British deal is raising investors’ confidence that agreements can be reached in the more complicated trade talks that lie ahead, specifically negotiations between US and Chinese officials kicking off this weekend. Trump said that the 145% levy against China, the world’s largest crude-importer, could be lowered if talks go well. “The real driver of risk assets today appears to be renewed optimism around progress in the US–China trade talks,” said Rebecca Babin, a senior energy trader at CIBC Private Wealth Group. “It’s also worth noting that sentiment toward crude remains overwhelmingly bearish.” Crude has slid since Trump took office on concerns that his global trade war will dent economic growth and slow energy demand. Adding to the bearishness, OPEC+ has decided to revive idled output faster than expected. Already, the drop in oil prices is spurring American shale producers to cut spending in the Permian Basin. Still, small pockets of bullishness are visible in the options market. There was active trading of Brent $95 September call options, which profit when futures rise. The US on Thursday sanctioned a third Chinese “teapot” oil refinery and various other entities associated with Iran, days ahead of a fourth round of nuclear talks between Washington and Tehran. The failure of the negotiations could push Brent up toward $70 a barrel, Citigroup analysts including Eric Lee said in a note. In the US,

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Indian LNG Buyers Embrace USA Benchmark to Balance Volatility

Indian liquefied natural gas importers have signed a flurry of long-term purchase agreements linked to the US price benchmark, the latest effort by the nation’s buyers to protect themselves from volatile markets. State-owned companies have signed at least four contracts since December, totaling nearly 11 million tons per year, priced to the Henry Hub index, according to the executives familiar with the deals. Until now, most of India’s long-term contracts have been linked to crude oil, the traditional way to price LNG deals. Pricing the fuel to the Henry Hub index doesn’t necessarily mean that the fuel will come from the US, rather it is a move to hedge risk.  India’s consumers — from power plants to petrochemical facilities — are highly price-sensitive as gas competes head-to-head with cheaper and dirtier alternatives. Companies that relied on the spot market or oil-linked contracts have periodically been forced to cut back purchases due to price spikes. US gas futures have also been relatively less volatile and more liquid than the Asian spot benchmark, the Japan-Korea Marker. “The last ten year average shows that there have been periods during winter months JKM benchmark surged beyond imagination, while Henry Hub prices saw proportionally smaller growth,” Bharat Petroelum Corp Ltd’s Director Finance V.R.K. Gupta said. BPCL in February signed a deal with ADNOC Trading for 2.5 million tons of LNG for five years. The Mumbai-based refiner will evaluate the performance of the deal and may sign more such contracts, Gupta said.  Indian Oil Corp. last week signed a deal with Trafigura for 2.5 million tons, or 27 cargoes, spread over five years, with supplies starting the middle of this year. The recent deals have been signed at a 115% link to Henry Hub plus $5 to $6 per million British thermal units. The supply is

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PJM, utilities urge FERC to dismiss call for colocation settlement talks

The Federal Energy Regulatory Commission should reject a call for a 90-day pause in its deliberations over the PJM Interconnection’s rules for colocating data centers at power plants, according to PJM, major utilities and other organizations. “The national interest will be best served by a quick dismissal of this proceeding, and a ruling that the existing PJM Tariff remains just and reasonable,” PJM transmission owners said in a Wednesday filing urging FERC to dismiss a call for stakeholder settlement talks. “Rather than fighting about a wish list of new rules, the parties will then instead begin to focus on obtaining service under the rules in place today.” The transmission owners include utility companies such as American Electric Power, Dominion Energy, Duke Energy, Exelon, FirstEnergy and PPL Electric. “The record is clear — no matter how connected to the PJM transmission system, large loads pose both a safety and a reliability concern,” the utilities said. “It is unrealistic to ask the [transmission owners] to accede to these demands in the context of settlement procedures while those questions remain unresolved.” PJM also wants FERC to ignore the call for settlement discussions that was made in late April by the Electric Power Supply Association, the PJM Power Providers Group, Calpine, Cogentrix Energy Power Management, Constellation Energy Generation and LS Power Development. “The Commission should not pause its work on offering the industry guidance on a path forward for co-location arrangements,” PJM said in a Monday filing. The call for settlement talks lacks broad stakeholder support, PJM said, noting it is holding a workshop on “large load” issues on Friday. American Municipal Power, a wholesale power provider for public power utilities, and Northern Virginia Electric Cooperative and Northeastern Rural Electric Membership Corp. also oppose holding settlement talks. Beside the power generators and trade organizations,

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IRA’s fate unclear as Republicans look to finance megabill

Dive Brief: The Inflation Reduction Act – which passed in 2022 without any Republican support and is anticipated to cost taxpayers between $780 billion and $2 trillion over its first ten years – is likely to be targeted for cuts as the Republican-controlled Congress aims to cut spending by $2 trillion in order to cut taxes by $4.5 trillion. However, certain provisions of the IRA have won support with Republican lawmakers, setting up likely disagreements over cuts in the budget reconciliation process. That process is already expected to be “very contentious,” said Harry Godfrey, who leads Advanced Energy United’s federal investment and manufacturing working group. “[House Ways and Means Committee Chairman Jason Smith] and the Ways and Means Republicans will need revenue and will be seeking it, and have been saying all along that the IRA is an area they’re going to look at,” said Ryan Abraham, a principal with Ernst & Young’s Washington Council advisory practice. “But obviously there are some concerns among some members.” Dive Insight: Abraham noted the May 1 letter sent by 26 House Republicans to Chairman Smith, advocating for the preservation of the IRA’s 45U, 45Y, and 48E tax credits. The letter advocates on behalf of nuclear power specifically, “[urging Smith] to maintain federal investment in the existing nuclear energy fleet while accelerating deployment of the next generation of nuclear power technologies.” The 45U credit is the IRA’s zero-emission nuclear power production credit, while 48E and 45Y are technology-neutral credits, which were targeted in legislation introduced in April by Rep. Julie Fedorchak, R-N.D. “There’s a lot of concern that some of the bonus items that have been created in the IRA, like direct pay and transferability, which were also in that Fedorchak bill, could also get targeted,” Abraham said. However, he said, “Chairman Smith is aware

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Energy Department Aligns Award Criteria for For-profit, Non-profit Organizations, and State and Local Governments, Saving $935 Million Annually

WASHINGTON — The U.S. Department of Energy (DOE) today announced three new policy actions that are projected to save more than $935 million annually for the American taxpayer, while expanding American innovation and scientific research. In three new policy memorandums, the DOE announced that it will follow best practices used by fellow grant providers and limit “indirect costs” of DOE funding to 10% for state and local governments, 15% for non-profit organizations, and 15% for for-profit companies. The Energy Department expects to generate over $935 million in annual cost savings for the American people, delivering on President Trump’s commitment to bring greater transparency and efficiency to federal government spending. Estimated savings are based on applying the new policies to 2024 fiscal year spending. “This action ensures that Department of Energy funds are supporting state, local, for-profit and non-profit initiatives that make energy more affordable and secure for Americans, not funding administrative costs,” U.S. Secretary of Energy Chris Wright said. “By aligning our policy on indirect costs with industry standards, we are increasing accountability of taxpayer dollars and ensuring the American people are getting the greatest value possible from these DOE programs.” These policy actions follow an announcement made in April to limit financial support of “indirect costs” of DOE research funding at colleges and universities to 15%, saving an estimated additional $405 million annually. By enacting indirect cost limits, the Department aligns its practices with those common for other grant providers. The full three memorandums are available below: POLICY FLASH SUBJECT: Adjusting Department of Energy Financial Assistance Policy for State and Local Governments’ Financial Assistance Awards BACKGROUND: Pursuant to 5 U.S.C. 553(a)(2), the Department of Energy (“Department”) is updating its policy with respect to Department financial assistance funding awarded to state and local governments. Through its financial assistance programs (which include grants and cooperative agreements),

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Tech CEOs warn Senate: Outdated US power grid threatens AI ambitions

The implications are clear: without dramatic improvements to the US energy infrastructure, the nation’s AI ambitions could be significantly constrained by simple physical limitations – the inability to power the massive computing clusters necessary for advanced AI development and deployment. Streamlining permitting processes The tech executives have offered specific recommendations to address these challenges, with several focusing on the need to dramatically accelerate permitting processes for both energy generation and the transmission infrastructure needed to deliver that power to AI facilities, the report added. Intrator specifically called for efforts “to streamline the permitting process to enable the addition of new sources of generation and the transmission infrastructure to deliver it,” noting that current regulatory frameworks were not designed with the urgent timelines of the AI race in mind. This acceleration would help technology companies build and power the massive data centers needed for AI training and inference, which require enormous amounts of electricity delivered reliably and consistently. Beyond the cloud: bringing AI to everyday devices While much of the testimony focused on large-scale infrastructure needs, AMD CEO Lisa Su emphasized that true AI leadership requires “rapidly building data centers at scale and powering them with reliable, affordable, and clean energy sources.” Su also highlighted the importance of democratizing access to AI technologies: “Moving faster also means moving AI beyond the cloud. To ensure every American benefits, AI must be built into the devices we use every day and made as accessible and dependable as electricity.”

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Networking errors pose threat to data center reliability

Still, IT and networking issues increased in 2024, according to Uptime Institute. The analysis attributed the rise in outages due to increased IT and network complexity, specifically, change management and misconfigurations. “Particularly with distributed services, cloud services, we find that cascading failures often occur when networking equipment is replicated across an entire network,” Lawrence explained. “Sometimes the failure of one forces traffic to move in one direction, overloading capacity at another data center.” The most common causes of major network-related outages were cited as: Configuration/change management failure: 50% Third-party network provider failure: 34% Hardware failure: 31% Firmware/software error: 26% Line breakages: 17% Malicious cyberattack: 17% Network overload/congestion failure: 13% Corrupted firewall/routing tables issues: 8% Weather-related incident: 7% Configuration/change management issues also attributed for 62% of the most common causes of major IT system-/software-related outages. Change-related disruptions consistently are responsible for software-related outages. Human error continues to be one of the “most persistent challenges in data center operations,” according to Uptime’s analysis. The report found that the biggest cause of these failures is data center staff failing to follow established procedures, which has increased by about 10 percentage points compared to 2023. “These are things that were 100% under our control. I mean, we can’t control when the UPS module fails because it was either poorly manufactured, it had a flaw, or something else. This is 100% under our control,” Brown said. The most common causes of major human error-related outages were reported as:

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Liquid cooling technologies: reducing data center environmental impact

“Highly optimized cold-plate or one-phase immersion cooling technologies can perform on par with two-phase immersion, making all three liquid-cooling technologies desirable options,” the researchers wrote. Factors to consider There are numerous factors to consider when adopting liquid cooling technologies, according to Microsoft’s researchers. First, they advise performing a full environmental, health, and safety analysis, and end-to-end life cycle impact analysis. “Analyzing the full data center ecosystem to include systems interactions across software, chip, server, rack, tank, and cooling fluids allows decision makers to understand where savings in environmental impacts can be made,” they wrote. It is also important to engage with fluid vendors and regulators early, to understand chemical composition, disposal methods, and compliance risks. And associated socioeconomic, community, and business impacts are equally critical to assess. More specific environmental considerations include ozone depletion and global warming potential; the researchers emphasized that operators should only use fluids with low to zero ozone depletion potential (ODP) values, and not hydrofluorocarbons or carbon dioxide. It is also critical to analyze a fluid’s viscosity (thickness or stickiness), flammability, and overall volatility. And operators should only use fluids with minimal bioaccumulation (the buildup of chemicals in lifeforms, typically in fish) and terrestrial and aquatic toxicity. Finally, once up and running, data center operators should monitor server lifespan and failure rates, tracking performance uptime and adjusting IT refresh rates accordingly.

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Cisco unveils prototype quantum networking chip

Clock synchronization allows for coordinated time-dependent communications between end points that might be cloud databases or in large global databases that could be sitting across the country or across the world, he said. “We saw recently when we were visiting Lawrence Berkeley Labs where they have all of these data sources such as radio telescopes, optical telescopes, satellites, the James Webb platform. All of these end points are taking snapshots of a piece of space, and they need to synchronize those snapshots to the picosecond level, because you want to detect things like meteorites, something that is moving faster than the rotational speed of planet Earth. So the only way you can detect that quickly is if you synchronize these snapshots at the picosecond level,” Pandey said. For security use cases, the chip can ensure that if an eavesdropper tries to intercept the quantum signals carrying the key, they will likely disturb the state of the qubits, and this disturbance can be detected by the legitimate communicating parties and the link will be dropped, protecting the sender’s data. This feature is typically implemented in a Quantum Key Distribution system. Location information can serve as a critical credential for systems to authenticate control access, Pandey said. The prototype quantum entanglement chip is just part of the research Cisco is doing to accelerate practical quantum computing and the development of future quantum data centers.  The quantum data center that Cisco envisions would have the capability to execute numerous quantum circuits, feature dynamic network interconnection, and utilize various entanglement generation protocols. The idea is to build a network connecting a large number of smaller processors in a controlled environment, the data center warehouse, and provide them as a service to a larger user base, according to Cisco.  The challenges for quantum data center network fabric

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Zyxel launches 100GbE switch for enterprise networks

Port specifications include: 48 SFP28 ports supporting dual-rate 10GbE/25GbE connectivity 8 QSFP28 ports supporting 100GbE connections Console port for direct management access Layer 3 routing capabilities include static routing with support for access control lists (ACLs) and VLAN segmentation. The switch implements IEEE 802.1Q VLAN tagging, port isolation, and port mirroring for traffic analysis. For link aggregation, the switch supports IEEE 802.3ad for increased throughput and redundancy between switches or servers. Target applications and use cases The CX4800-56F targets multiple deployment scenarios where high-capacity backbone connectivity and flexible port configurations are required. “This will be for service providers initially or large deployments where they need a high capacity backbone to deliver a primarily 10G access layer to the end point,” explains Nguyen. “Now with Wi-Fi 7, more 10G/25G capable POE switches are being powered up and need interconnectivity without the bottleneck. We see this for data centers, campus, MDU (Multi-Dwelling Unit) buildings or community deployments.” Management is handled through Zyxel’s NebulaFlex Pro technology, which supports both standalone configuration and cloud management via the Nebula Control Center (NCC). The switch includes a one-year professional pack license providing IGMP technology and network analytics features. The SFP28 ports maintain backward compatibility between 10G and 25G standards, enabling phased migration paths for organizations transitioning between these speeds.

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Engineers rush to master new skills for AI-driven data centers

According to the Uptime Institute survey, 57% of data centers are increasing salary spending. Data center job roles that saw the highest increases were in operations management – 49% of data center operators said they saw highest increases in this category – followed by junior and mid-level operations staff at 45%, and senior management and strategy at 35%. Other job categories that saw salary growth were electrical, at 32% and mechanical, at 23%. Organizations are also paying premiums on top of salaries for particular skills and certifications. Foote Partners tracks pay premiums for more than 1,300 certified and non-certified skills for IT jobs in general. The company doesn’t segment the data based on whether the jobs themselves are data center jobs, but it does track 60 skills and certifications related to data center management, including skills such as storage area networking, LAN, and AIOps, and 24 data center-related certificates from Cisco, Juniper, VMware and other organizations. “Five of the eight data center-related skills recording market value gains in cash pay premiums in the last twelve months are all AI-related skills,” says David Foote, chief analyst at Foote Partners. “In fact, they are all among the highest-paying skills for all 723 non-certified skills we report.” These skills bring in 16% to 22% of base salary, he says. AIOps, for example, saw an 11% increase in market value over the past year, now bringing in a premium of 20% over base salary, according to Foote data. MLOps now brings in a 22% premium. “Again, these AI skills have many uses of which the data center is only one,” Foote adds. The percentage increase in the specific subset of these skills in data centers jobs may vary. The Uptime Institute survey suggests that the higher pay is motivating workers to stay in the

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Microsoft will invest $80B in AI data centers in fiscal 2025

And Microsoft isn’t the only one that is ramping up its investments into AI-enabled data centers. Rival cloud service providers are all investing in either upgrading or opening new data centers to capture a larger chunk of business from developers and users of large language models (LLMs).  In a report published in October 2024, Bloomberg Intelligence estimated that demand for generative AI would push Microsoft, AWS, Google, Oracle, Meta, and Apple would between them devote $200 billion to capex in 2025, up from $110 billion in 2023. Microsoft is one of the biggest spenders, followed closely by Google and AWS, Bloomberg Intelligence said. Its estimate of Microsoft’s capital spending on AI, at $62.4 billion for calendar 2025, is lower than Smith’s claim that the company will invest $80 billion in the fiscal year to June 30, 2025. Both figures, though, are way higher than Microsoft’s 2020 capital expenditure of “just” $17.6 billion. The majority of the increased spending is tied to cloud services and the expansion of AI infrastructure needed to provide compute capacity for OpenAI workloads. Separately, last October Amazon CEO Andy Jassy said his company planned total capex spend of $75 billion in 2024 and even more in 2025, with much of it going to AWS, its cloud computing division.

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John Deere unveils more autonomous farm machines to address skill labor shortage

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Self-driving tractors might be the path to self-driving cars. John Deere has revealed a new line of autonomous machines and tech across agriculture, construction and commercial landscaping. The Moline, Illinois-based John Deere has been in business for 187 years, yet it’s been a regular as a non-tech company showing off technology at the big tech trade show in Las Vegas and is back at CES 2025 with more autonomous tractors and other vehicles. This is not something we usually cover, but John Deere has a lot of data that is interesting in the big picture of tech. The message from the company is that there aren’t enough skilled farm laborers to do the work that its customers need. It’s been a challenge for most of the last two decades, said Jahmy Hindman, CTO at John Deere, in a briefing. Much of the tech will come this fall and after that. He noted that the average farmer in the U.S. is over 58 and works 12 to 18 hours a day to grow food for us. And he said the American Farm Bureau Federation estimates there are roughly 2.4 million farm jobs that need to be filled annually; and the agricultural work force continues to shrink. (This is my hint to the anti-immigration crowd). John Deere’s autonomous 9RX Tractor. Farmers can oversee it using an app. While each of these industries experiences their own set of challenges, a commonality across all is skilled labor availability. In construction, about 80% percent of contractors struggle to find skilled labor. And in commercial landscaping, 86% of landscaping business owners can’t find labor to fill open positions, he said. “They have to figure out how to do

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2025 playbook for enterprise AI success, from agents to evals

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More 2025 is poised to be a pivotal year for enterprise AI. The past year has seen rapid innovation, and this year will see the same. This has made it more critical than ever to revisit your AI strategy to stay competitive and create value for your customers. From scaling AI agents to optimizing costs, here are the five critical areas enterprises should prioritize for their AI strategy this year. 1. Agents: the next generation of automation AI agents are no longer theoretical. In 2025, they’re indispensable tools for enterprises looking to streamline operations and enhance customer interactions. Unlike traditional software, agents powered by large language models (LLMs) can make nuanced decisions, navigate complex multi-step tasks, and integrate seamlessly with tools and APIs. At the start of 2024, agents were not ready for prime time, making frustrating mistakes like hallucinating URLs. They started getting better as frontier large language models themselves improved. “Let me put it this way,” said Sam Witteveen, cofounder of Red Dragon, a company that develops agents for companies, and that recently reviewed the 48 agents it built last year. “Interestingly, the ones that we built at the start of the year, a lot of those worked way better at the end of the year just because the models got better.” Witteveen shared this in the video podcast we filmed to discuss these five big trends in detail. Models are getting better and hallucinating less, and they’re also being trained to do agentic tasks. Another feature that the model providers are researching is a way to use the LLM as a judge, and as models get cheaper (something we’ll cover below), companies can use three or more models to

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OpenAI’s red teaming innovations define new essentials for security leaders in the AI era

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI has taken a more aggressive approach to red teaming than its AI competitors, demonstrating its security teams’ advanced capabilities in two areas: multi-step reinforcement and external red teaming. OpenAI recently released two papers that set a new competitive standard for improving the quality, reliability and safety of AI models in these two techniques and more. The first paper, “OpenAI’s Approach to External Red Teaming for AI Models and Systems,” reports that specialized teams outside the company have proven effective in uncovering vulnerabilities that might otherwise have made it into a released model because in-house testing techniques may have missed them. In the second paper, “Diverse and Effective Red Teaming with Auto-Generated Rewards and Multi-Step Reinforcement Learning,” OpenAI introduces an automated framework that relies on iterative reinforcement learning to generate a broad spectrum of novel, wide-ranging attacks. Going all-in on red teaming pays practical, competitive dividends It’s encouraging to see competitive intensity in red teaming growing among AI companies. When Anthropic released its AI red team guidelines in June of last year, it joined AI providers including Google, Microsoft, Nvidia, OpenAI, and even the U.S.’s National Institute of Standards and Technology (NIST), which all had released red teaming frameworks. Investing heavily in red teaming yields tangible benefits for security leaders in any organization. OpenAI’s paper on external red teaming provides a detailed analysis of how the company strives to create specialized external teams that include cybersecurity and subject matter experts. The goal is to see if knowledgeable external teams can defeat models’ security perimeters and find gaps in their security, biases and controls that prompt-based testing couldn’t find. What makes OpenAI’s recent papers noteworthy is how well they define using human-in-the-middle

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