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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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Veteran Gas Executive Leaving Mercuria

Steve Hill, who was hired by Mercuria Energy Group in 2024 to build out its liquefied natural gas business, is leaving the trading house. Hill was part of the company’s efforts to expand into the fast-growing global LNG market. Before joining, he was responsible for the vast LNG, gas and power marketing and trading business at energy giant Shell Plc. He was one of a trio of heavyweight hires Mercuria made after reaping bumper profits, setting off a renewed push into trading physical commodities, along with Kostas Bintas in metals and Nick O’Kane in gas and power. Known as one of the world’s biggest traders of oil and gas, the firm has been a relative latecomer behind other trading house rivals in building out a large-scale physical trading business for LNG. During Hill’s relatively brief tenure, Mercuria signed deals to offtake LNG from Oman, as well as supply Turkey and China. He also hired several of his former colleagues from Shell, though one — Singapore-based Dong Yuan — recently left the company. A spokesperson for Mercuria confirmed Hill is leaving the company. Hill didn’t immediately respond to a request for comment. WHAT DO YOU THINK? Generated by readers, the comments included herein do not reflect the views and opinions of Rigzone. All comments are subject to editorial review. Off-topic, inappropriate or insulting comments will be removed.

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Crude Settles Higher After Volatile Week

Oil edged higher at the end of a volatile week, as traders weighed tensions in Iran and positive sentiment in wider markets. West Texas Intermediate settled near $60 a barrel after plunging 4.6% on Thursday, the most since June. President Donald Trump said in a social media post that he “greatly” respects Iran’s decision to cancel scheduled hangings of protesters. His rhetoric over recent days has reduced expectations of an immediate US response to violent protests in the Islamic Republic, which could have led to disruptions to the country’s roughly 3.3 million barrel-per-day oil production, as well as shipping. Nevertheless, Washington is boosting its military presence in the Middle East. At least one aircraft carrier is moving into the region and other military assets are expected to be shifted there in the coming days and weeks, Fox News reported, citing military sources. Traders have in the past covered bearish wagers ahead of the weekend in periods of heightened geopolitical risks. “While the risk of imminent intervention from the US against Iran has subsided, it’s pretty clear that the risk is still present, which should keep the market on its toes in the short term,” said Warren Patterson, head of commodities strategy at ING Groep NV. “However, the longer this goes on without a US response, the risk premium will continue to evaporate, allowing more bearish fundamentals to take center stage.” Disruption to Kazakh exports from the Black Sea, short-term tightness in the North Sea and a host of financial flows from options markets to commodity index rebalancing have also helped lift an oil market coming off its biggest drop since 2020 on rising supplies. In a sign that lower prices are starting to bite, Harold Hamm, the billionaire wildcatter who helped kick off the US shale revolution, said his firm

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U.S. Energy Secretary and Slovakia’s Prime Minister Sign Agreement to Advance U.S.-Slovakia Civil Nuclear Program

WASHINGTON—U.S. Secretary of Energy Chris Wright and Slovak Prime Minister Robert Fico today signed an Intergovernmental Agreement (IGA) to advance cooperation on Slovakia’s civil nuclear power program. This landmark agreement includes the development of a new, state-owned American 1,200 MWe nuclear unit at the Jaslovské Bohunice Nuclear Power Plant, deepening the U.S.-Slovakia strategic partnership and strengthening European energy security. The agreement builds on President Trump’s commitment to advancing American energy leadership. A project of this scale is expected to create thousands of American jobs across engineering, advanced manufacturing, construction, nuclear fuel services, and project management, while reinforcing U.S. supply chains and expanding access to global markets for American-made nuclear technology. These efforts lay the foundation for sustained U.S. engagement in Slovakia’s nuclear energy program and support future civil nuclear projects across the region. It also supports Slovakia’s efforts to diversify its energy supply, strengthen long-term energy security, and integrate advanced American nuclear technology into Central Europe’s energy infrastructure. “The United States is proud to partner with Slovakia as a trusted ally as we expand cooperation across the energy sector,” said Energy Secretary Chris Wright. “Today’s civil nuclear agreement reflects our shared commitment to strengthening European energy security and sovereignty for decades to come. By deploying America’s leading nuclear technology, we are creating thousands of good-paying American jobs, expanding global markets for U.S. nuclear companies, and driving economic growth at home”. “I see this moment as a significant milestone in our bilateral relations, but also as a clear signal that Slovakia and the United States are united by a common strategic thinking about the future of energy – about its safety, sustainability, and technological maturity,” said the Prime Minister of the Slovak Republic Robert Fico. The planned nuclear unit represents a multibillion-dollar energy infrastructure investment and one of the largest in

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Valero to Cut 200+ Jobs as California Refinery Closes

Valero Energy Corp. plans to let go of 237 employees at its Benicia refinery as it winds down operations at one of California’s few remaining fuel-making plants. Valero expects the shutdown to be permanent and 237 jobs will be cut March 15 to July 1, the company said in a letter to California’s employment regulator and local officials. Those losing jobs are not represented by a union and represent the bulk of the plant’s 348-person staff.  “We do not plan to coordinate services with the local workforce development board or any other entity,” refinery manager Lauren Bird, whose position is being eliminated, said in the letter. The Texas-based oil company announced in 2025 plans to close the plant and last-ditch efforts by Governor Gavin Newsom, regulators and local officials to keep the gates open were unsuccessful. Multiple California refineries have closed or converted to making biofuels in recent years, dwindling fuel supply in a state where drivers regularly pay the highest gasoline prices in the nation. Last week, Newsom praised plans by Valero to continue supplying the state with gasoline amid the shutdown, saying the decision to import fuel to the region was a constructive development from an earlier possibility of a full-on exit. WHAT DO YOU THINK? Generated by readers, the comments included herein do not reflect the views and opinions of Rigzone. All comments are subject to editorial review. Off-topic, inappropriate or insulting comments will be removed.

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Trump Administration Calls for Emergency Power Auction to Build Big Power Plants Again

WASHINGTON—U.S. Secretary of Energy Chris Wright and Secretary of the Interior Doug Burgum, vice-chair and chair of the National Energy Dominance Council (NEDC) respectively, today joined Mid-Atlantic governors urging PJM Interconnection, L.L.C. (PJM) to temporarily overhaul its market rules to strengthen grid reliability and reduce electricity costs for American families and businesses by building more than $15 billion of reliable baseload power generation.  The initiative calls on PJM to conduct an emergency procurement auction to address escalating electricity prices and growing reliability risks across the mid-Atlantic region of the United States. The action follows a series of PJM policies over the years that have weakened the electric grid, including the premature shutdown of reliable power generation.  President Trump declared a National Energy Emergency on his first day in office, warning that the previous administrations energy subtraction agenda left the country vulnerable to blackouts and soaring electricity prices. During the Biden administration, PJM forced nearly 17 gigawatts of reliable baseload power generation offline. For the first time in history, PJM’s capacity auction failed to secure enough generation resources to meet basic reliability requirements. If not fixed, it will lead to further rising prices and blackouts.  “High electricity prices are a choice,” said Energy Secretary Chris Wright. “The Biden administration’s forceful closures of coal and natural gas plants without reliable replacements left the United States in an energy emergency. Perhaps no region in America is more at risk than in PJM. That’s why President Trump asked governors across the Mid-Atlantic to come together and call upon PJM to allow America to build big reliable power plants again. Our directives will restore affordable and reliable electricity so American families thrive and America’s manufacturing industries once again boom. President Trump promised to unleash American energy and put the American people first. This plan keeps

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Russian Oil and Gas Revenue Falls to Lowest in 5 Years

Russia’s revenues from its oil and gas industry, vital to financing its war in Ukraine, dropped to a five-year low in 2025 as crude prices slumped and gas exports declined. The nation’s budget received a total of 8.48 trillion rubles ($108 billion) in oil and gas taxes last year, Finance Ministry said on Thursday. That’s 24 percent less than in 2024 and the lowest level since the start of the decade, historic figures show.  Russia, a top-three global oil producer and home to the world’s largest gas reserves, heavily relies on tax revenues from the two industries to fill its state coffers. The decline, mainly driven by a combination of weaker global oil prices, stronger ruble and energy sanctions against Russia, comes as the Kremlin has boosted military spending significantly above what it planned to fund the war, which is about to enter a fifth year. To bridge the widening gap between revenues and spending, the government in Moscow has eaten into more than half of the country’s National Wellbeing Fund – a buffer against economic shocks – and turned to expensive borrowings that will take years to pay back.   Oil revenues dropped more than 22 percent year on year to 7.13 trillion rubles, reaching the lowest level since 2023, Bloomberg calculations show. Concerns about an oversupply in the global crude market, and discounts for Russian barrels in particular due to western sanctions, hit the flow of money into state coffers. The official data show that the average price of Urals, Russia’s main oil-export blend, for tax purposes was $57.65 a barrel in 2025, a 15 percent drop from a year earlier.   Starting from November, when the US blacklisted two major oil producers Rosneft PJSC and Lukoil PJSC, the discount of Urals to the Brent benchmark widened to about $27 a barrel at

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NVIDIA’s Rubin Redefines the AI Factory

The Architecture Shift: From “GPU Server” to “Rack-Scale Supercomputer” NVIDIA’s Rubin architecture is built around a single design thesis: “extreme co-design.” In practice, that means GPUs, CPUs, networking, security, software, power delivery, and cooling are architected together; treating the data center as the compute unit, not the individual server. That logic shows up most clearly in the NVL72 system. NVLink 6 serves as the scale-up spine, designed to let 72 GPUs communicate all-to-all with predictable latency, something NVIDIA argues is essential for mixture-of-experts routing and synchronization-heavy inference paths. NVIDIA is not vague about what this requires. Its technical materials describe the Rubin GPU as delivering 50 PFLOPS of NVFP4 inference and 35 PFLOPS of NVFP4 training, with 22 TB/s of HBM4 bandwidth and 3.6 TB/s of NVLink bandwidth per GPU. The point of that bandwidth is not headline-chasing. It is to prevent a rack from behaving like 72 loosely connected accelerators that stall on communication. NVIDIA wants the rack to function as a single engine because that is what it will take to drive down cost per token at scale. The New Idea NVIDIA Is Elevating: Inference Context Memory as Infrastructure If there is one genuinely new concept in the Rubin announcements, it is the elevation of context memory, and the admission that GPU memory alone will not carry the next wave of inference. NVIDIA describes a new tier called NVIDIA Inference Context Memory Storage, powered by BlueField-4, designed to persist and share inference state (such as KV caches) across requests and nodes for long-context and agentic workloads. NVIDIA says this AI-native context tier can boost tokens per second by up to 5× and improve power efficiency by up to 5× compared with traditional storage approaches. The implication is clear: the path to cheaper inference is not just faster GPUs.

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Power shortages, carbon capture, and AI automation: What’s ahead for data centers in 2026

“Despite a broader use of AI tools in enterprises and by consumers, that does not mean that AI compute, AI infrastructure in general, will be more evenly spread out,” said Daniel Bizo, research director at Uptime Institute, during the webinar. “The concentration of AI compute infrastructure is only increasing in the coming years.” For enterprises, the infrastructure investment remains relatively modest, Uptime Institute found. Enterprises will limit investment to inference and only some training, and inference workloads don’t require dramatic capacity increases. “Our prediction, our observation, was that the concentration of AI compute infrastructure is only increasing in the coming years by a couple of points. By the end of this year, 2026, we are projecting that around 10 gigawatts of new IT load will have been added to the global data center world, specifically to run generative AI workloads and adjacent workloads, but definitely centered on generative AI,” Bizo said. “This means these 10 gigawatts or so load, we are talking about anywhere between 13 to 15 million GPUs and accelerators deployed globally. We are anticipating that a majority of these are and will be deployed in supercomputing style.” 2. Developers will not outrun the power shortage The most pressing challenge facing the industry, according to Uptime, is that data centers can be built in less than three years, but power generation takes much longer. “It takes three to six years to deploy a solar or wind farm, around six years for a combined-cycle gas turbine plant, and even optimistically, it probably takes more than 10 years to deploy a conventional nuclear power plant,” said Max Smolaks, research analyst at Uptime Institute. This mismatch was manageable when data centers were smaller and growth was predictable, the report notes. But with projects now measured in tens and sometimes hundreds of

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Google warns transmission delays are now the biggest threat to data center expansion

The delays stem from aging transmission infrastructure unable to handle concentrated power demands. Building regional transmission lines currently takes seven to eleven years just for permitting, Hanna told the gathering. Southwest Power Pool has projected 115 days of potential loss of load if transmission infrastructure isn’t built to match demand growth, he added. These systemic delays are forcing enterprises to reconsider fundamental assumptions about cloud capacity. Regions including Northern Virginia and Santa Clara that were prime locations for hyperscale builds are running out of power capacity. The infrastructure constraints are also reshaping cloud competition around power access rather than technical capabilities. “This is no longer about who gets to market with the most GPU instances,” Gogia said. “It’s about who gets to the grid first.” Co-location emerges as a faster alternative to grid delays Unable to wait years for traditional grid connections, hyperscalers are pursuing co-location arrangements that place data centers directly adjacent to power plants, bypassing the transmission system entirely. Pricing for these arrangements has jumped 20% in power-constrained markets as demand outstrips availability, with costs flowing through to cloud customers via regional pricing differences, Gogia said. Google is exploring such arrangements, though Hanna said the company’s “strong preference is grid-connected load.” “This is a speed to power play for us,” he said, noting Google wants facilities to remain “front of the meter” to serve the broader grid rather than operating as isolated power sources. Other hyperscalers are negotiating directly with utilities, acquiring land near power plants, and exploring ownership stakes in power infrastructure from batteries to small modular nuclear reactors, Hanna said.

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OpenAI turns to Cerebras in a mega deal to scale AI inference infrastructure

Analysts expect AI workloads to grow more varied and more demanding in the coming years, driving the need for architectures tuned for inference performance and putting added pressure on data center networks. “This is prompting hyperscalers to diversify their computing systems, using Nvidia GPUs for general-purpose AI workloads, in-house AI accelerators for highly optimized tasks, and systems such as Cerebras for specialized low-latency workloads,” said Neil Shah, vice president for research at Counterpoint Research. As a result, AI platforms operating at hyperscale are pushing infrastructure providers away from monolithic, general-purpose clusters toward more tiered and heterogeneous infrastructure strategies. “OpenAI’s move toward Cerebras inference capacity reflects a broader shift in how AI data centers are being designed,” said Prabhu Ram, VP of the industry research group at Cybermedia Research. “This move is less about replacing Nvidia and more about diversification as inference scales.” At this level, infrastructure begins to resemble an AI factory, where city-scale power delivery, dense east–west networking, and low-latency interconnects matter more than peak FLOPS, Ram added. “At this magnitude, conventional rack density, cooling models, and hierarchical networks become impractical,” said Manish Rawat, semiconductor analyst at TechInsights. “Inference workloads generate continuous, latency-sensitive traffic rather than episodic training bursts, pushing architectures toward flatter network topologies, higher-radix switching, and tighter integration of compute, memory, and interconnect.”

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Cisco’s 2026 agenda prioritizes AI-ready infrastructure, connectivity

While most of the demand for AI data center capacity today comes from hyperscalers and neocloud providers, that will change as enterprise customers delve more into the AI networking world. “The other ecosystem members and enterprises themselves are becoming responsible for an increasing proportion of the AI infrastructure buildout as inferencing and agentic AI, sovereign cloud, and edge AI become more mainstream,” Katz wrote. More enterprises will move to host AI on premises via the introduction of AI agents that are designed to inject intelligent insight into applications and help improve operations. That’s where the AI impact on enterprise network traffic will appear, suggests Nolle. “Enterprises need to host AI to create AI network impact. Just accessing it doesn’t do much to traffic. Having cloud agents access local data center resources (RAG etc.) creates a governance issue for most corporate data, so that won’t go too far either,” Nolle said.  “Enterprises are looking at AI agents, not the way hyperscalers tout agentic AI, but agents running on small models, often open-source, and are locally hosted. This is where real AI traffic will develop, and Cisco could be vulnerable if they don’t understand this point and at least raise it in dialogs where AI hosting comes up,” Nolle said. “I don’t expect they’d go too far, because the real market for enterprise AI networking is probably a couple years out.” Meanwhile, observers expect Cisco to continue bolstering AI networking capabilities for enterprise branch, campus and data centers as well as hyperscalers, including through optical support and other gear.

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Microsoft tells communities it will ‘pay its way’ as AI data center resource usage sparks backlash

It will work with utilities and public commissions to set the rates it pays high enough to cover data center electricity costs (including build-outs, additions, and active use). “Our goal is straightforward: To ensure that the electricity cost of serving our data centers is not passed on to residential customers,” Smith emphasized. For example, the company is supporting a new rate structure Wisconsin that would charge a class of “very large customers,” including data centers, the true cost of the electricity required to serve them. It will collaborate “early, closely, and transparently” with local utilities to add electricity and supporting infrastructure to existing grids when needed. For instance, Microsoft has contracted with the Midcontinent Independent System Operator (MISO) to add 7.9GW of new electricity generation to the grid, “more than double our current consumption,” Smith noted. It will pursue ways to make data centers more efficient. For example, it is already experimenting with AI to improve planning, extract more electricity from existing infrastructure, improve system resilience, and speed development of new infrastructure and technologies (like nuclear energy). It will advocate for state and national public policies that ensure electricity access that is affordable, reliable, and sustainable in neighboring communities. Microsoft previously established priorities for electricity policy advocacy, Smith noted, but “progress has been uneven. This needs to change.” Microsoft is similarly committed when it comes to data center water use, promising four actions: Reducing the overall amount of water its data centers use, initially improving it by 40% by 2030. The company is exploring innovations in cooling, including closed-loop systems that recirculate cooling liquids. It will collaborate with local utilities to map out water, wastewater, and pressure needs, and will “fully fund” infrastructure required for growth. For instance, in Quincy, Washington, Microsoft helped construct a water reuse utility that recirculates

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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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