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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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DOE and SBA Launch SBIC-E Initiative to Unleash Private Capital for American Innovation and Small Businesses

WASHINGTON—The U.S. Department of Energy (DOE) and the U.S. Small Business Administration (SBA) today signed a Memorandum of Agreement establishing the Small Business Investment Company-Energy (SBIC-E) Initiative, a new strategic partnership advancing President Trump’s commitment to supporting America’s small businesses, strengthening domestic manufacturing and supply chains, and ensuring the United States leads in the technologies critical to our national and economic security. The new SBIC-E Initiative brings together DOE’s scientific and technical expertise with SBA’s proven Small Business Investment Company (SBIC) Program, which currently has $58 billion in combined portfolio value. Since 1958, the SBIC Program has invested $147 billion in American small businesses, and since 1995, SBIC-backed businesses have created or supported 10.6 million jobs. “America’s small businesses drive American innovation and affordable, reliable energy access,” said U.S. Secretary of Energy Chris Wright. “By partnering with the Small Business Administration, the Energy Department is committing to invest its resources in American small businesses that will create jobs, strengthen our domestic manufacturing base, and unleash American energy production.” Through DOE’s Office of Technology Commercialization (OTC), the Department will identify strategic technology priorities, provide technical and commercialization expertise, and help engage the investment community. SBA, through its Office of Investment and Innovation, will administer the initiative and encourage the formation and growth of investment funds focused on those priorities. SBIC-E adds another tool to that effort by connecting innovators with private capital to help promising technologies grow, scale, and build here at home. “President Trump is establishing American energy dominance, ending the Green New Scam, and putting our nations’ producers and innovators back in control at the dawn of a new era of energy reliability and abundance,” said SBA Administrator Kelly Loeffler. “Through this partnership, the SBA and Department of Energy are strengthening access to capital in the private sector to

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Energy Department Announces $500 Million Award to Revitalize American Steelmaking

WASHINGTON—The U.S. Department of Energy (DOE) today announced a $500 million award to support a $1 billion investment at Cleveland-Cliffs’ Middletown Works facility in Middletown, Ohio. Vice President JD Vance and U.S. Energy Secretary Chris Wright visited Middletown Works today to highlight the Trump Administration’s commitment to American steelworkers and the resurgence of American manufacturing. The investment will modernize American steelmaking, protect 2,300 American jobs, and strengthen the domestic steel supply chain. The project advances President Trump’s commitment to put American workers first, bring investment back to American communities, and strengthen the industries critical to America’s economic and national security. Cleveland-Cliffs determined that the business case for the original project scope no longer made sense given customers’ unwillingness to pay a “green premium” for steel. Working with DOE, Cleveland-Cliffs identified a viable alternative that will upgrade and improve the efficiency of the existing coal-fired blast furnace while also capturing and commercializing co-product blast furnace gas (BFG). “President Trump is rebuilding America’s industrial base,” said Secretary Wright. “This investment puts American workers and American manufacturing first. It will modernize one of our nation’s critical steelmaking facilities, protect thousands of jobs, and strengthen our domestic steel production—keeping Ohio at the heart of American manufacturing and strengthening our national security.” The investment will modernize critical steelmaking operations at Middletown Works by rebuilding and upgrading the plant’s main coal-fired ironmaking furnace, deploying AI to optimize furnace operations and improve energy efficiency, and building an on-site facility to convert steel mill process gases into electricity. Follow-on investments will turn industrial byproducts into materials for concrete used in regional infrastructure. “This landmark investment at Middletown Works will secure a reliable domestic supply of high-purity steel while protecting thousands of quality jobs in Ohio,” said Assistant Secretary of Energy Audrey Robertson. “DOE is proud to partner with Cleveland-Cliffs to reduce America’s dependence on foreign products

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Energy Secretary Keeps Critical Generation Available in Mid-Atlantic

WASHINGTON—U.S. Secretary of Energy Chris Wright today issued an emergency order to address critical grid reliability issues facing the Mid-Atlantic region of the United States. The emergency order directs PJM Interconnection L.L.C. (PJM), in coordination with Constellation Energy Corporation, to ensure Units 3 and 4 of the Eddystone Generating Station in Pennsylvania remain available to operate and to employ economic dispatch to minimize costs for the American people. The units were originally slated to shut down on May 31, 2025. “The energy sources that perform when you need them most are the most valuable,” Secretary Wright said. “During recent Mid-Atlantic heat waves, coal, natural gas, and nuclear kept the lights and air conditioners on. President Trump and the Energy Department are committed to keeping critical generation available when demand is highest, reducing the risk of blackouts and ensuring Americans have affordable, reliable, and secure power—regardless of whether the wind is blowing or the sun is shining.” As outlined in DOE’s Resource Adequacy Report, power outages could increase by 100 times in 2030 if the U.S. continues to take reliable power offline. This order is in effect beginning on August 23, 2026, through November 20, 2026.                                                                                             ###

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Energy Department Announces $500 Million to Secure America’s Critical Mineral and Battery Supply Chains

WASHINGTON—The U.S. Department of Energy’s (DOE) Office of Critical Minerals and Energy Innovation (CMEI) today announced $500 million for seven selected projects to expand critical mineral and material processing, battery manufacturing, and recycling capacity in the United States. In accordance with President Trump’s Executive Order, Unleashing American Energy, the selected projects advance the President’s agenda to strengthen America’s domestic critical minerals and materials supply chains, reduce reliance on foreign sources, bolster national security, and advance American energy dominance. “For too long, America has depended on foreign actors for critical materials essential to modern life that underpin our economy, energy security, and national security,” said U.S. Secretary of Energy Chris Wright. “President Trump is reversing that dependence by securing our critical supply chains, unleashing American industry, and bringing critical materials production and processing back to the United States.” “DOE is taking decisive action to secure the critical supply chains necessary to power our nation,” said Assistant Secretary of Energy Audrey Robertson. “These projects underscore DOE’s commitment to driving innovation, reducing reliance on foreign sources, and promoting American energy dominance.” This is the third round of funding from DOE’s Battery Materials Processing and Battery Manufacturing and Recycling programs, which support battery materials processing, recycling, and manufacturing projects. These include demonstration projects, construction of commercial-scale facilities, and retrofitting or retooling existing facilities.  Critical minerals and materials are essential to American industry, energy production, and national security. Expanding domestic capacity will help ensure the resources America needs are processed, manufactured, and recycled in the United States.  Information on the selected projects is available here and here.

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bp lets Shah Deniz compression automation contract

bp has let a contract to Emerson to deliver automation technologies for the Shah Deniz Compression project offshore Azerbaijan. Emerson will provide integrated control and safety systems aimed at enhancing production, safety, and reliability on the new offshore compression platform. The contract includes systems to provide process control, safety shutdown, fire and gas detection, and power management. Together, these systems deliver real-time visibility and remote control of critical operations, Emerson said. The $2.9 billion Shah Deniz Compression project, which includes an electrically powered, normally unattended offshore production platform, is a next stage development of the Caspian Sea Shah Deniz field. Designed to access low-pressure gas reserves and maximize overall recovery, the platform will be equipped with four 11 Mw compressors and serve as the central compression hub for gas from the Shah Deniz Alpha and Bravo platforms. The platform will operate remotely from bp’s onshore Sangachal terminal 55 km south of Baku. The project is expected to enable about 50 billion cu m of additional gas and about 25 million bbl of condensate production and export. Construction is scheduled to be completed in 2029, with first gas compression expected from the Shah Deniz Alpha platform in 2029 and from the Shah Deniz Bravo platform in 2030. The agreement follows a previous automation contract bp signed with Emerson for the Azeri Central East and Shah Deniz Stage 2 developments. bp is operator at Shah Deniz (29.99%) with partners Lukoil (19.99%), TPAO (19%), Cenub Qaz Dehlizi (16.02%), NICO (10%), and MVM (5%).

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Federal court voids Texas GulfLink license over agency’s ‘serious procedural errors’

The ruling voids the license, halting all construction or progress. Sentinel Midstream declined comment on the ruling and would not answer questions about the status of construction. GulfLink, sited about 30 miles offshore Freeport, Tex., is designed to export up to 1 million b/d via Very Large Crude Carriers (VLCCs) to the government of Japan and Freeport Commodities. The project involves a 44-mile, 42-in. OD pipeline and was scheduled to begin operations around 2028. The estimated $2.1 billion investment was funded as part of a broader trade agreement between the US and Japan. The legal battle stems from a specific rule in the Deepwater Port Act of 1974 that dictates that the federal government can only permit one crude oil deepwater port, including any supporting infrastructure, within a single designated “application area.” Because the competing SPOT project’s pipeline route physically overlaps and intersects GulfLink’s lines, the plaintiff—Citizens for Clean Air & Clean Water in Brazoria County (Better Brazoria), represented by Earthjustice—successfully argued that MARAD violated the “one port” rule when issuing GulfLink’s license in February. The three-judge panel found that MARAD “improperly drew” the map designing the project’s official boundaries to exclude the pipelines and approved two overlapping projects in the same zone instead of only licensing one. The court wrote that the scope of the error made vacatur, not the less serious remand without vacatur, the appropriate remedy. Vacatur deems the license invalid and is used when the court finds “serious procedural errors” that cannot be easily explained or fixed with minor changes. Remand without vacatur sends the decision back to the agency for corrections but leaves the current license in place in the meantime. SPOT project status The $2.5-3-billion SPOT project, developed by Enterprise Products Partners in partnership with Enbridge Inc., also lies about 30 miles from Freeport. Designed to handle VLCCs,

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IBM unveils dual-architecture processor to run Arm-native apps on Z mainframes

“These caches have enormously low latency, and that is one of the key reasons and key engineering choices to support the performance and scalability of enterprise workloads, very data-intensive workloads like databases and transactions,” Jacobi said. “In addition, we have an on-chip data processing unit for IO acceleration and dedicated AI accelerators as well as accelerators for data compression, cryptography and data sorting.” One of the biggest takeaways from this processor announcement is that the enormous catalog of software already built for Arm becomes accessible on a mainframe without anyone having to port it first, notes Matt Kimball, senior datacenter analyst at Moor Insights & Strategy, in a research note about the news. Still, “this is a 2027 conversation, and with no date, supported software list, or Arm licensing treatment, the work now is inventory and scenario planning rather than financial modeling,” Kimball wrote.

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PJM’s New Data Center Power Equation

PJM Interconnection has now filed one of the most consequential proposed changes yet in the relationship between data centers and the electric grid. Rather than simply treating a new hyperscale or AI facility like any other customer whose demand will be backed through regional capacity procurement, PJM is proposing a framework under which the largest new loads would need to be supported by new capacity, have their needs covered through the Reliability Backstop Procurement, or face potential curtailment when the regional power system is short of supply. The approach has been developing since PJM launched its Critical Issue Fast Path process for large loads in 2025, but it became substantially more concrete in late July and August 2026. PJM filed its proposed Reliability Backstop Procurement with FERC on July 31 and began accepting applications that day for its FERC-approved Expedited Interconnection Track. On Aug. 13, PJM filed its proposed Interim Resource Adequacy Service, or IRAS, along with the Large Load Registry that would support it. The immediate numbers explain the urgency. PJM’s July 2026 capacity auction for the 2028/2029 delivery year procured 138,318 MW of unforced capacity through the centralized auction. Even after including Fixed Resource Requirement resources, however, PJM came up 6,831 MW short of its reliability requirement. The auction cleared at the FERC-approved $325/MW-day price cap. It was the second consecutive auction in which the PJM region failed to procure its full reliability requirement, something that had not happened before these two auctions. That gap is occurring while demand continues to accelerate. PJM’s 2026 long-term forecast projects summer peak demand growing at an average 3.6% annually over the next decade, compared with just 0.3% in the comparable forecast issued in 2021. Summer peak demand is projected to rise by nearly 66 GW over 10 years. Data centers are

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Zayo, NVIDIA Build the Long-Haul Backbone for Distributed AI

The data center industry’s increasingly power-first approach to site selection has created a follow-on question: Once the megawatts are found, is there enough network infrastructure to make the site useful at AI scale? Zayo and NVIDIA are putting real infrastructure behind that question. Zayo said it is working with NVIDIA to expand network capacity supporting AI factories across North America, including an 8,000-route-mile program targeting some of the fastest-growing AI corridors in the United States. The project encompasses six new long-haul routes along with overbuilds of existing network across 10 high-demand corridors. The announcement arrives as AI data center development moves beyond the largest established hubs toward markets where power and land may be more readily available, but fiber capacity cannot necessarily be taken for granted. That geography is increasingly important. NVIDIA has separately developed “scale-across” networking technology designed to allow AI infrastructure distributed among different buildings — or even data centers separated by hundreds of kilometers — to operate as a more unified computing environment. Put together, the developments suggest that networking is becoming inseparable from the AI factory buildout itself. Power may determine where the next generation of AI infrastructure can be built. Fiber will increasingly determine how effectively those sites can participate in the larger AI ecosystem. Fiber Follows the Power Zayo CEO Steve Smith said AI demand is changing both where network infrastructure is needed and how aggressively capacity must be deployed ahead of development. “AI is fundamentally reshaping where and how network infrastructure needs to be built across the U.S.,” Smith said. The company’s 8,000-mile program is more nuanced than that top-line number might suggest. Zayo disclosed in April that the expansion includes approximately 3,000 route miles across six new long-haul routes, plus more than 5,000 route miles of overbuilds across 10 existing corridors. Zayo

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Southern’s 17 GW Pipeline Puts AI Power Demand Into Utility Math

The headline number from Southern Company’s latest earnings report is hard to miss: electricity use by data centers across the utility’s system increased 55% in the second quarter compared with a year earlier. But the more consequential numbers may be the ones sitting behind it. Southern now has more than 1.2 GW of operating data center load, up by more than 500 MW from a year ago. At the same time, its electric utilities have signed contracts and large-load agreements totaling more than 17 GW by the mid-2030s, with another 8 GW in late-stage development and a prospective pipeline of large industrial and data center projects exceeding 75 GW. That leaves an enormous gap between the data center megawatts consuming electricity today and the load Southern has contractually positioned itself to serve during the next decade. For the data center industry, that gap may be the most important part of Southern’s second-quarter story. It offers a look at how utilities are beginning to convert the AI infrastructure boom from forecasts and campus announcements into contracts, generation procurement, transmission investment and eventually energized capacity. From Contracts to Megawatts Southern added roughly 6 GW of contracted large load during the quarter alone. Alabama Power signed three projects representing about 3 GW, while Georgia Power reached a 25-year agreement to serve OpenAI’s planned project in Effingham County near Savannah. That facility is expected to require approximately 3.2 GW and begin taking electric service in phases in 2028. The numbers nevertheless require an important distinction. Seventeen gigawatts contracted does not mean 17 GW will suddenly appear on Southern’s grid. Large data center campuses ramp gradually, often over several years, and Southern executives acknowledged that actual customer ramp schedules do not always match the assumptions made when projects are first approved. CEO Chris Womack said

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PORTS-Pike Takes Shape as an 8-GW AI Infrastructure Model

Back on March 31, 2026, we discussed we discussed SoftBank and SB Energy’s plans to redevelop the former Portsmouth Gaseous Diffusion Plant site near Piketon as a 10-GW artificial intelligence data center campus supported by almost an equal amount of new power generation. At the time, the plan called for as much as 10 GW of new generation, including 9.2 GW of natural gas capacity, along with approximately $4.2 billion of high-voltage transmission infrastructure developed with AEP Ohio. An initial 800-MW data center phase was targeted for service in 2028. The March story was notable because Pike County appeared to offer a preview of a new model for building hyperscale infrastructure: develop the generation, transmission and data center simultaneously rather than wait for an increasingly congested regional grid to deliver multiple gigawatts of capacity. Not to mention the reuse of a brownfield site with the encouragement of the federal government. Since then, almost every important part of the project has moved forward, and on August 17, the most consequential missing pieces fell into place. NVIDIA announced that it will become the exclusive AI compute infrastructure provider for the PORTS-Pike Technology Campus. OpenAI will be the data center customer, signing a 20-year lease with SB Energy for approximately 8 GW of IT capacity. NVIDIA will invest another $1.5 billion in SB Energy and provide credit support for the land, power and shell infrastructure behind an initial 4.25 GW of IT load, with an option covering approximately another 3.75 GW. The Securities and Exchange Commission filing accompanying the announcement makes the financial commitment even more significant. NVIDIA disclosed that its aggregate payment obligation associated with its initial commitment is capped at $105 billion. That is not a conventional capital commitment to spend $105 billion building the campus, nor is it simply a

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Nvidia scales back financing guarantee for OpenAI data center

Nvidia is scaling back a proposed financial guarantee tied to a massive OpenAI data center project in Ohio, reducing its initial commitment from as much as $250 billion to less than $120 billion, according to report in the Wall Street Journal. Earlier this month, Nvidia announced partnerships with major financial firms including Apollo Global Management, BlackRock, Blackstone, Brookfield Asset Management, Goldman Sachs and KKR, aimed at mobilizing more than $500 billion in capital for AI computing infrastructure. The change represents a significant restructuring of Nvidia’s role in financing the planned facility, which is being developed by SB Energy, a subsidiary of SoftBank. Under the revised arrangement, Nvidia would guarantee financing for the project’s first phase, representing roughly 5 gigawatts of capacity, or half of the total proposed capacity. Financing for the remaining capacity would be considered separately at a later stage.

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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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