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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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Energy Secretary Keeps Guam Power On

WASHINGTON—U.S. Secretary of Energy Chris Wright today issued an emergency order to mitigate the risk of blackouts for hardworking families and businesses in Guam. The emergency order permits the Guam Power Authority (GPA) to operate specified generation units to meet anticipated electricity demand and maintain reliability. The order follows a request from GPA. “President Trump and the Department of Energy remain committed to using every available tool to reduce the risk of power outages and lower energy costs for hardworking families and businesses,” said Secretary Wright. “Today’s order responds to the urgent need to strengthen grid reliability while ensuring the people of Guam have access to affordable, reliable, and secure electricity.” GPA has limited generation options to meet demand due to limitations, including but not limited to, constraints with the Ukudu steam turbine going offline for emergency repairs. This order is effective 10:00 AM Guam Standard Time on August 6, 2026, through 11:59 PM Guam Standard Time on November 4, 2026.                                                                                            ###

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DOE’s Office of Energy Dominance Financing Closes Loan to Strengthen Puerto Rico’s Grid, Delivering Hundreds of Millions in Electricity Cost Savings

WASHINGTON – The U.S. Department of Energy’s (DOE) Office of Energy Dominance Financing (EDF) today announced it has closed a $489.4 million loan to Amanecer Puerto Rico LLC, a subsidiary of Pattern Energy, to lower electricity costs and strengthen Puerto Rico’s electric grid. Thanks to President Trump’s Working Families Tax Cuts Act, the investment is expected to save Puerto Rican families and businesses approximately $312.5 million in electricity costs over the next 25 years while improving grid reliability, strengthening energy security, supporting American manufacturing, and reducing dependence on foreign-controlled supply chains. “President Trump’s Working Families Tax Cuts Act is driving investments that strengthen America’s energy infrastructure while lowering costs for hardworking families,” said EDF Director Gregory A. Beard. “This investment will strengthen Puerto Rico’s electric grid, lower electricity costs, support American manufacturing, and provide a pathway for the reliable, dispatchable power needed to deliver affordable, reliable, and secure energy for the people of Puerto Rico.” Puerto Rico’s grid has experienced chronic outages and prolonged service interruptions that have imposed significant costs on families, businesses, and critical infrastructure. Following a comprehensive review by the Trump Administration, DOE restructured the financing to better align with the Administration’s priorities of lowering energy costs, strengthening American manufacturing, and ensuring the deployment of reliable, secure energy infrastructure. The financing will support: 220 megawatts of battery energy storage systems in Arecibo and Santa Isabel using American-manufactured battery technology and secure domestic supply chains. Battery storage capable of providing backup electricity for more than 100,000 customers during power shortages and helping avoid approximately 13 million customer interruption hours based on 2025 operating data. A pathway for the future development of reliable, dispatchable natural gas-fired generation to improve grid stability and strengthen Puerto Rico’s long-term energy security. This financing builds on the Trump Administration’s broader efforts to restore

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United States to Host International Atomic Energy Agency Launch for New Maritime Nuclear Initiative

WASHINGTON—The United States will host the ministerial launch of the International Atomic Energy Agency’s (IAEA) new initiative, Atomic Technologies Licensed for Applications at Sea (ATLAS), in Washington, D.C., on August 26–27, 2026. This landmark event will bring together ministers, policymakers, and industry leaders from around the world to advance the safe and secure use of nuclear technologies in the maritime sector. The ATLAS initiative aims to create an international framework to address legal and regulatory complexities to enable the deployment of nuclear applications at sea. It builds upon the IAEA’s extensive experience and global authority in nuclear safety, security, and safeguards to support the deployment of innovative civil nuclear technologies. The initiative is technology-neutral and focuses on establishing and maintaining the highest standards for safety, security, and nonproliferation. “DOE remains focused on unleashing American energy dominance, accelerating innovation, and advancing sources of energy that are affordable, reliable, and secure for the American people,” said U.S. Secretary of Energy Chris Wright. “Hosting the launch of ATLAS supports this critical mission, positioning the U.S. nuclear and maritime sectors at the forefront of advanced energy innovation, while promoting safety and security for the United States and the world.”  Leading up to the launch, the United States will hold an “Industry Day” on August 25, 2026. This event will serve as a platform for America’s leading nuclear and maritime companies to showcase cutting-edge technologies. The Industry Day will highlight American innovation and underscore American energy dominance that will support the global expansion of civil maritime nuclear applications. “The global maritime sector is at a critical turning point, facing urgent pressure to sustain long-distance, high-speed operations while ensuring reliability and energy security,” said IAEA Director General Rafael Mariano Grossi. “Nuclear energy is fast emerging as a game-changer. Small modular reactors offer a safe and viable option for

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Vista seeks RIGI approval for $5.8 billion Bandurria Norte development

Vista Energy SAB de CV has applied to include its Bandurria Norte shale oil development in Argentina’s Large Investment Incentive Regime (RIGI). The $5.8-billion project targets peak production of 50,000 boe/d. Bandurria Norte is the largest oil project submitted under RIGI, which provides tax, customs, and foreign-exchange incentives for qualifying investments, and follows approval of Pampa Energía SA’s $4.521 billion Rincón de Aranda development, which established the first framework for qualifying incremental shale oil production under the regime. Together, the projects represent more than $10.3 billion in planned investment and would extend RIGI-backed development into undeveloped Vaca Muerta oil acreage. Bandurria Norte spans 26,500 acres in Vaca Muerta’s oil window and currently has no producing wells. Vista plans to drill and complete 332 horizontal wells and build dedicated infrastructure, including a 40,000-b/d oil treatment plant, a gas compression plant, gathering systems, pipelines, and associated infrastructure. The project would be Vista’s first large-scale development outside its core Bajada del Palo hub, where existing roads, processing capacity, and gathering networks support lower-cost drilling. Bandurria Norte requires full greenfield development, increasing upfront capital requirements, and execution risk. Vista said RIGI incentives are material to developing its undeveloped acreage because incremental production from new areas can qualify separately from existing output if volumes remain physically and operationally traceable. Export capacity remains critical Bandurria Norte forms part of Vista’s plan to increase production to 208,000 boe/d in 2028 and 250,000 boe/d in 2030. Development depends on additional crude transportation capacity from the Neuquén basin, particularly the Vaca Muerta Oil Sur (VMOS) pipeline under construction between Allen and Punta Colorada in Río Negro province. Designed for an initial capacity of 550,000 b/d and expandable to 700,000 b/d, VMOS is scheduled for start-up in first-half 2027. Vista averaged 156,000 boe/d of production in second-quarter 2026, up 16%

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Oil prices surge on renewed Middle East tensions

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Türkiye signs partnership deal with bp for Kirkuk oil field redevelopment

bp plc has farmed out a 15% interest in BP Energy Co. of Kirkuk Ltd. (BP ECKL) to state-owned Turkish Petroleum Corp. (TPAO), expanding on a partnership to support the redevelopment of major oil and gas fields in the Kirkuk region of northern Iraq. The move comes as Iraq aims to increase oil and gas production through various international partnerships. Signed during the official visit of Iraqi Prime Minister Ali Al-Zaidi to Türkiye, the agreement builds on a strategic cooperation memorandum of understanding (MoU) signed by the companies in February 2026.  The development and production contract covers an initial phase of oil and gas production of more than 3 billion boe from the Baba and Avanah domes of Kirkuk oil field and the adjacent Bai Hassan, Jambur, and Khabbaz fields in Federal Iraq, all currently operated by the North Oil Co. (NOC) and North Gas Co. (NGC), bp said in a release July 28. The contract area holds potential for additional exploration, the companies said. The deal follows one that saw ConocoPhillips acquire a 42% interest in BP ECKL. Together, bp said, the transactions support the next phase of redevelopment in Kirkuk. Türkiye Energy and Natural Resources Minister Bayraktar said the agreement is a step “towards making Turkish Petroleum a company that produces 1 million barrels of oil and natural gas per day.” Following completion of the transaction, which is subject to regulatory approvals, bp will remain the majority shareholder and a key participant in BP ECKL (bp 43%, ConocoPhillips 42%, TPAO 15%).

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Polish data center plans to send its waste heat to the neighbors

As Europe swelters in a heatwave, residents probably don’t want to hear about ways to make their homes even hotter, but that’s what Polish property developer Citylink is talking about, with plans to dump waste heat from a new data center in Wrocław into the municipal district heating network. Citylink is designing the data center so that heat from servers can be recovered instead of being dissipated via cooling systems — and as the data center grows, any increase in computing power will mean more energy available for recovery. The collaboration with local power company Kogeneracja will provide “valuable experience in designing and operating modern data centers, with a particular focus on infrastructure dedicated to AI nodes,” said Michał Starybrat, development director at Citylink.

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The Data Center Industry’s Permission to Build

The data center industry has spent the past several years announcing the future. Gigawatts. AI factories. New regions. New power architectures. Campuses at a scale that would have seemed extraordinary before generative AI reset the industry’s expectations. Now the public has entered the room. Communities are asking harder questions about who pays for electrical infrastructure, where the water comes from, how much noise reaches neighboring properties and what remains locally after construction crews leave. Utilities are being pressed to protect ratepayers from speculative load and costly system upgrades. Elected officials who once treated data centers primarily as economic-development wins are finding that the politics have changed. The defining question is no longer whether demand is real. It is whether the data center industry can keep earning the permission required to build at the scale it has promised. I mean permission in a broader sense than zoning approval, an environmental permit or a signed utility agreement. I mean the political and social room to develop infrastructure measured in hundreds of megawatts and billions of dollars—often in places whose residents have only recently begun to understand what is being proposed around them. That room is narrowing. A Different Kind of Constraint On July 18, opponents organized 142 demonstrations across 42 states in what Reuters described as the first coordinated national protest against the data center buildout. The movement crossed familiar political boundaries, bringing together environmental advocates, rural landowners and residents concerned about power prices, water, noise and the pace of development. A June Reuters/Ipsos poll found that 57% of respondents would oppose a data center in their community. Only 14% said they would be comfortable with one nearby. Those findings deserve the industry’s full attention. New York has imposed a one-year pause on certain environmental approvals for new hyperscale data centers while

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NVIDIA’s Reported $50B Lease and the Nuclear-Powered AI Factory

Aalo and Crusoe Pursue the Nuclear-Powered AI Factory The Aalo-Crusoe partnership addresses the industry’s power problem by bringing power generation directly to the compute. In this case, skipping intermediary power stages such as minimal grid or custom BTM gas turbine solutions and going straight to nuclear. Aalo Atomics and Crusoe said they plan to deploy a Crusoe Spark modular data center running Crusoe Cloud at Idaho National Laboratory in 2027. The proof-of-concept project is intended to demonstrate an AI workload operating on power from an Aalo advanced reactor. Crusoe continues to expand their other data center campus projects. The companies then intend to deploy Aalo Pods, Aalo’s 50-megawatt-electric nuclear power plants, at Crusoe data centers by the end of 2029. Aalo has already begun work on a second reactor beside its initial test unit at the Idaho site. That reactor is expected to produce electricity for the Crusoe installation. On July 4, 2026, Aalo’s zero-power Critical Test Reactor reached criticality, sustaining a nuclear chain reaction without generating commercial electricity. The test reactor contains a full-scale core and components analogous to those planned for the 10-megawatt-electric Aalo-X power reactor being built next door, but it operates before sodium coolant and electricity-generating systems are added. Aalo plans to continue experiments with the Critical Test Reactor to refine its reactor-physics models, characterize control behavior and generate data supporting development and licensing of the full-power Aalo-X system. Advanced nuclear announcements sometimes blur the line between a successful test, an electricity-producing demonstration and a commercially licensed fleet. Aalo has achieved an important technical milestone, but substantial work remains before reactors can be manufactured, licensed, financed and operated at commercial data center sites. The pairing with Crusoe should be noted because it connects a reactor developer with a company that can provide the data center load,

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Data Center Jobs: Engineering, Construction, Commissioning, Sales, Field Service and Facility Tech Jobs Available in Major Data Center Hotspots

Each month Data Center Frontier, in partnership with Pkaza, posts some of the hottest data center career opportunities in the market. Here’s a look at some of the latest data center jobs posted on the Data Center Frontier jobs board, powered by Pkaza Critical Facilities Recruiting. Looking for Data Center Candidates? Check out Pkaza’s Active Candidate / Featured Candidate Hotlist  CFD Engineer – Data Center Mechanical DesignNew York, NY (remote)This position is also available as a remote role anywhere in the US in addition to key markets such as Cedar Rapids, IA; Kansas City, CA or White Plains, NY. Our client is an engineering design and commissioning company that has a national footprint and specializes in MEP critical facilities design. They provide design, commissioning, consulting and management expertise in the critical facilities space. They have a mindset to provide reliability, energy efficiency, and sustainable design expertise when providing these consulting services for enterprise, colocation and hyperscale companies. This career-growth minded opportunity offers exciting projects with leading-edge technology and innovation as well as competitive salaries and benefits. Electrical Commissioning Agent – Data CentersColumbus, OH (limited travel) Non-traveling CxA positions available in: Indianapolis, IN; Cedar Rapids, IA; Phoenix, AZ; Atlanta, GA and Austin, TX. Traveling CxA based really near any major airport, otherwise traveling to: New York, NY; White Plains, NY; Dallas, TX; Richmond, VA; Montvale, NJ; Charlotte, NC; Salt Lake City, UT; Kansas City, MO; Chesterton, IN or Chicago, IL. ***Also looking for a lead EE and ME CxA Agents and CxA PMs.*** This opportunity is with a leading EPC company of data center design / build / commissioning solutions. This company provides a complete life cycle of solutions that are custom-fit to the requirements of their client’s mission-critical facilities. This opportunity provides a career-growth minded role with exciting projects with leading-edge technology

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Navigating Virginia’s Data Center Boom: Policy Shifts, Local Projects, and Future Challenges

Virginia’s newest high profile data center story is no longer the announcement of the next generation AI data center campus, it is now how the state is beginning to set the trend for legislative process to protect its communities while still encouraging the data center industry development. On August 3, state Senators Richard Stuart, a Republican, and Russet Perry, a Democrat, called on Gov. Abigail Spanberger to convene a special legislative session to address groundwater strain. Their request followed a state study warning that eastern Virginia’s groundwater supply is constrained and that large new industrial withdrawals may be difficult to sustain. The debate has expanded into calls for a broader pause: Senator Glen Sturtevant has asked for an immediate statewide moratorium on new data center development, while Senate President Pro Tempore Louise Lucas has said such a moratorium deserves serious consideration. Those proposals are not yet law, but they are the clearest indication that Virginia’s policy discussion has moved beyond incremental regulation. The Commonwealth spent years treating data centers primarily as an economic-development and tax-base success. It is now evaluating them simultaneously as power, water, land-use, air-quality and ratepayer issues. That shift is especially important for projects outside Northern Virginia, where developers are increasingly pursuing large sites in communities with less experience reviewing hyperscale infrastructure. The calls for a special session arrive only weeks after a significant package of data center laws and budget provisions took effect July 1. Virginia’s new budget established what the administration describes as a first-of-its-kind electricity consumption tax on data centers. The charge is 1.1 cents per kilowatt-hour, began July 1 and is capped at $600 million in annual collections, with excess revenue refunded to data center taxpayers. The compromise preserved Virginia’s sales-and-use-tax exemption for qualifying data center equipment, avoiding the abrupt repeal sought by

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Land and Expand: The Gigawatt Credibility Test

The midsummer wave of U.S. data center development is not defined by a single market, developer or technology company. It stretches from the Georgia coast to West Texas, from the industrial Midwest to the Mississippi River. What links the projects announced since early June is not just their scale, it is the realization that scale alone is not enough. Developers are still announcing multibillion-dollar campuses and gigawatt power requirements, but the language surrounding those announcements has changed. Companies are emphasizing who will pay for new generation and transmission, how cooling systems will limit water consumption, what communities will receive beyond temporary construction employment, and when contracted customers will begin occupying capacity. In several cases, the announcement is less about acquiring land than proving that a project has become commercially and electrically credible.  As we have seen progressing through the industry, the latest announcements point toward campuses that combine compute, power, financing and community agreements in one development package. OpenAI Goes Direct in Georgia OpenAI, on July 22 disclosed Project Camellia, a long-term data center development in Effingham County, Georgia. OpenAI said it is designing and developing the campus itself and has contracted with Georgia Power for 3.2 gigawatts of electricity, to be delivered in phases from 2028 through 2032. The project has been reported as a roughly $20 billion investment on approximately 1,400 acres, making it one of the largest individual data center proposals currently moving through the U.S. pipeline. Project Camellia is notable not only for its size but for OpenAI’s more direct role. The company has traditionally secured capacity through cloud providers and infrastructure partners. By taking responsibility for designing and developing the Georgia campus, OpenAI is signaling that control over power, schedule and facility design has become strategically important as AI companies compete for increasingly scarce large-scale

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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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