In this in-depth conversation, Professor J. Nathan Kutz — Director of Physics-Informed AI at Autodesk and one of the leading figures in data-driven modeling, dynamical systems, and scientific machine learning — shares his journey from academia to industry and reflects on how AI is reshaping engineering. Known for influential contributions to methods such as Dynamic Mode Decomposition and Sparse Identification of Nonlinear Dynamics, Kutz offers a rare perspective on the evolution of machine learning in the physical sciences, the role of physics in building trustworthy AI systems, and the future of automation, agents, and human expertise in engineering design.Key topicsHistory of machine learning in engineeringDynamic Mode Decomposition (DMD) and Sparse Identification of Nonlinear Dynamics (SINDy)Physics-informed AI and reduced order modelingThe debate between physics-based and data-driven modelsThe future of autonomous agents and their impact on industryPapers

Flower discrimination by pollinators in a dynamic chemical environment — Jeffrey A. Riffell, Eli Shlizerman, Elischa Sanders, Leif Abrell, Billie Medina, Armin J. Hinterwirth, J. Nathan Kutz

https://doi.org/10.1126/science.1251041

Nathan’s early move into neuroscience and data-driven biological modeling.

Data assimilation and discrepancy modeling with shallow recurrent decoders — Yuxuan Bao, J. Nathan Kutz

https://arxiv.org/abs/2512.01170

Using ML to close the gap between simulation and reality.

Discovering governing equations from data by sparse identification of nonlinear dynamical systems — Steven L. Brunton, Joshua L. Proctor, J. Nathan Kutz

https://doi.org/10.1073/pnas.1517384113

The foundational paper introducing SINDy.

On Dynamic Mode Decomposition: Theory and Applications — Jonathan H. Tu, Clarence W. Rowley, Dirk M. Luchtenburg, Steven L. Brunton, J. Nathan Kutz

https://doi.org/10.3934/jcd.2014.1.391

A key reference for Dynamic Mode Decomposition.

Data-driven discovery of partial differential equations — Samuel H. Rudy, Steven L. Brunton, Joshua L. Proctor, J. Nathan Kutz

https://doi.org/10.1126/sciadv.1602614

Extends equation discovery to PDEs and physical systems.

Deep learning for universal linear embeddings of nonlinear dynamics — Bethany Lusch, J. Nathan Kutz, Steven L. Brunton

https://doi.org/10.1038/s41467-018-07210-0

Connects deep learning with Koopman theory.

Articraft: An Agentic System for Scalable Articulated 3D Asset Generation — Matt Zhou, Ruining Li, Xiaoyang Lyu, Zhaomou Song, Zhening Huang, Chuanxia Zheng, Christian Rupprecht, Andrea Vedaldi, Shangzhe Wu

https://arxiv.org/abs/2605.15187

Project page: https://articraft3d.github.io/

A practical example of agentic AI for engineering design.Chapters00:40 Introduction to Episode

05:00 Welcoming Prof Kutz10:34 The Evolution of Data-Driven Modeling16:13 Understanding the SINDy Algorithm and Its Implications22:14 Comparing Reduced Order Modeling and Modern Machine Learning28:29 The Role of Data in Machine Learning and Physics34:23 Challenges in Extrapolation and Real-World Applications40:46 Insights from McLaren and Team Dynamics46:07 The Shift from Academia to Industry48:53 Collaboration and Innovation in Engineering51:57 The Role of Human Expertise in Design54:45 Leveraging AI in Formula One57:32 The Future of AI and Workforce Dynamics59:06 Navigating Career Choices in a Changing Landscape01:03:02 The Evolution of Thought in Engineering01:09:06 Preparing for the Future of Technology01:14:04 Responsible Use of AI in Engineering

In this episode, Neil explores how agents, foundation models, and AI are set to transform the Computer-Aided Engineering (CAE) and Electronic Design Automation (EDA) landscapes. He shares a comprehensive historical perspective and predicts a near-future where AI-driven automation redefines engineering workflows, productivity, and innovation.

Main Topics:

• The evolution of simulation codes from the 1960s to modern commercial software
• The rise of cloud computing, GPUs, and their impact on CAE and EDA industries
• The integration of AI, surrogate modeling, and foundation models into simulation workflows
• The emergence of agentic AI systems capable of autonomously performing complex engineering tasks
• The strategic responses of major software companies to AI and agent technologies
• The potential democratization and automation of engineering design through AI agents
• Critical questions on model ownership, transparency, and industry adoption

Timestamps:

00:40 - Introduction: How agents and foundation models will disrupt CAE & EDA
01:40 - Historical overview: From code writing in the 60s to commercial software
03:10 - Growth of aerospace and automotive industry codes and commercialization
04:40 - The impact of HPC, cloud computing, and hardware evolution
06:25 - Rise of cloud SaaS models and "sassification" of simulation tools
07:40 - Big tech entrance: AWS, Microsoft, and Google in CAE & EDA
09:00 - GPU acceleration: Changed landscape in past three to four years
09:10 - The role of AI startups offering surrogate models and real-time simulation
10:40 - Industry consolidation: Mergers and acquisitions among software giants
11:40 - The emergence of foundation models and surrogate systems in simulation
13:00 - The significance of agents: Combining AI, models, and automation
14:10 - Capabilities of autonomous AI agents in complex engineering workflows
15:25 - Practical use cases: Running simulations, setting up experiments, and data analysis
16:40 - How agent-driven automation could democratize engineering expertise
16:10 - Questions about model ownership, open source codes, and licensing
19:40 - The future of AI in engineering: Collaboration, transparency, and scientific rigor
21:25 - Final thoughts: Opportunities, challenges, and the transformative potential of AI

* Please note that this a personal opinion and not that of NVIDIA

In this conversation, Neil Ashton and Prof. Siddhartha Mishra, and Prof. Johannes Brandstetter discuss their recent paper on AI foundation models in computational fluid dynamics (CFD). They explore the backgrounds of the speakers, the journey to writing the paper, the role of AI in CFD, and the challenges of scaling laws and data generation. The discussion also covers model training costs, open questions, and future directions for research in this field.

Fluid Intelligence: A Forward Look on AI Foundation Models in Computational Fluid Dynamics : https://arxiv.org/abs/2511.20455v1

In this episode, Neil Ashton discusses various conferences and workshops in the automotive, aerospace, and machine learning fields. He highlights the importance of these events for networking, education, and staying updated with industry trends. From the SAE and AIAA events to machine learning workshops, Neil provides insights into what attendees can expect and the value of participating in these gatherings.

In this episode of the Neil Ashton podcast, the host revisits key trends in Computational Fluid Dynamics (CFD) from the past year, focusing on the rise of GPUs, advancements in AI and machine learning, the shift to cloud computing, the increasing adoption of high fidelity methods, and ongoing mergers and acquisitions in the industry. Each trend is explored in depth, highlighting the implications for the future of engineering and technology

In this episode, Professor Brian Launder (Professor at the University of Manchester and Fellow of the Royal Society and Royal Academy of Engineers) shares his remarkable journey through academia, detailing his early fascination with heat transfer, his transition to MIT, and his significant contributions to turbulence modeling and computational fluid dynamics (CFD). We touch upon the key role that Professor Brian Spalding had on his career as well as work that led to the breakthrough k-epilson turbulence model as well as the pioneering work on second-moment closure model. Prof Launder highlights the key role of collaborators and ex students such as Professors Hector Iacovides, Tim Craft, Bill Jones, Kemal Hanjalić and many more. He ends with advice for early-stage researchers and reflections on more than 50 years worth of academic research.

Chapters

00:30 Introduction
05:00 Early Academic Journey
10:06 Transition to MIT and Research Focus
16:21 Return to Imperial College and Early Career
21:06 Research Projects and PhD Students
27:46 Development of the k-epilson model
33:18 CHAM and Career Changes
36:24 Move to UC Davis and New Research Directions
44:05 Challenges and Opportunities in Research
47:07 The Interview Experience
51:14 Transition to Manchester University
52:23 Research Innovations in Turbulence Modeling
57:45 The Development of the TCL Model
01:03:15 Nonlinear Eddy Viscosity Models
01:05:58 Advanced Wall Functions and Their Applications
01:10:09 Reflections on Career and Contributions
01:15:49 Legacy and Impact on Turbulence Modeling

Top Turbulence Modelling contributions (https://scholar.google.com/citations?user=Y3JbAK8AAAAJ&hl=en) 

In this episode, Joris Poort, CEO and founder of Rescale, shares his personal journey on founding Rescale as well as his thoughts on the future of CAE. He discusses the challenges of introducing HPC to the cloud market, the traits that make successful founders, and the importance of perseverance and execution in entrepreneurship. Joris reflects on the early days of Rescale, the significance of early investors, and the evolving landscape of cloud computing and AI integration in engineering. The conversation highlights the complexities of transitioning to cloud solutions and the future potential of HPC in various industries. In this conversation, Joris discusses the transformative impact of AI on engineering, particularly in the context of inference, simulation, and automation. He emphasizes the importance of efficiency in engineering processes and how AI can significantly reduce the time required for complex simulations. The discussion also touches on the cultural shifts within organizations as they adapt to AI technologies, the potential for AI surrogates to revolutionize engineering practices, and the challenges of closing the sim-to-real gap. Joris offers insights for aspiring founders, encouraging them to pursue meaningful work that can drive innovation and societal progress.

Chapters

00:00 Introductions
03:30 The Genesis of Rescale: A Cloud Computing Journey
05:21 From Engineering to Entrepreneurship: The Leap of Faith
09:28 Traits of a Successful Founder: Courage and Perseverance
14:51 Tactical Steps to Startup Success: Building from the Ground Up
22:10 Milestones and Breakthroughs: The Early Days of Rescale
30:54 Navigating Challenges: The Role of Cloud Providers in HPC
35:24 The Intersection of HPC and AI Training
37:05 Cloud vs On-Premise: The Cost Debate
39:54 Complexities of HPC in Enterprises
42:27 The Slow Shift to Cloud Adoption
44:34 Optimizing Workloads with Rescale
46:50 Usability Challenges in Enterprise Software
48:32 The Rise of Neo Clouds and Competition
51:18 Speed and Efficiency in AI Training
54:34 AI's Transformative Impact on Engineering
58:54 The Future of AI Surrogates in Design
01:03:28 Agentic AI: The New Paradigm in Engineering
01:14:21 Solving Real Business Problems
01:19:26 The Impact of AI on Engineering
01:22:27 Innovation in Aerospace and Beyond
01:25:19 Cultural Change in Organizations
01:28:34 The Future of AI and Engineering
01:39:09 Advice for Aspiring Founders

Keywords

HPC, cloud computing, startup journey, Rescale, entrepreneurship, AI, technology, innovation, engineering, business, AI, engineering, inference, simulation, automation, digital twin, innovation, aerospace, machine learning, technology

In this episode of the Neil Ashton podcast, Neil discusses the impact of AI on CAE engineering, providing five essential tips for engineers to thrive in this evolving landscape. The conversation covers the importance of maintaining an open mind, continuous education, and preparing for AI physics applications. It also delves into the build vs. buy dilemma for AI solutions and the emerging concept of agentic AI, which promises to revolutionize engineering practices.

Chapters

00:00 Introduction to the Podcast and AI in Engineering
01:03 Five Tips for CAE Engineers in the Era of A1
01:24 1: Keeping an Open Mind 
07:39 2: Understanding AI Physics and Its Applications
13:30 3: Preparing for AI Implementation in Engineering
18:54 4: The Build vs. Buy Dilemma in AI Solutions
22:20 5: The Future of Agentic AI in Engineering

In this conversation, Neil Ashton interviews Prof. Johannes Brandstetter, a physicist turned machine learning expert, about his journey from academia to industry, focusing on the application of machine learning in engineering and computational fluid dynamics (CFD). They discuss the Aurora project, the challenges of integrating machine learning with engineering, and the importance of data in training models. Johannes shares insights on the use of transformers in modeling, the significance of resolution independence, and the role of open-source practices in advancing the field. The conversation also touches on the challenges of founding a startup and the need for multidisciplinary collaboration in tackling complex engineering problems.

Links: 

Github: https://brandstetter-johannes.github.io
Emmi AI: https://www.emmi.ai
Google scholar: https://scholar.google.com/citations?user=KiRvOHcAAAAJ&hl=de

AB-UPT transform paper: https://arxiv.org/abs/2502.09692

Chapters

00:00 Introduction to Johannes Brandstetter
07:10 The Aurora Project and Key Learnings
11:15 Machine Learning in Engineering and CFD
17:19 Challenges with Mesh Graph Networks
20:16 Transformers in Physics Modeling
31:14 Tokenization in CFD with Transformers
39:58 Challenges in High-Dimensional Meshes
41:08 Inference Time and Mesh Generation
41:36 Neural Operators and CAD Geometry
45:59 Anchor Tokens and Scaling in CFD
48:40 Data Dependency and Multi-Fidelity Models
50:32 The Role of Physics in Machine Learning
54:28 Temporal Modeling in Engineering Simulations
56:58 Learning from Temporal Dynamics
1:00:58 Stability in Rollout Predictions
1:03:48 Multidisciplinary Approaches in Engineering
1:05:18 The Startup Journey and Lessons Learned

In this episode of the Neil Ashton podcast, Professor Russell Cummings shares his extensive journey through the fields of aerodynamics, computational fluid dynamics and hypersonics. He discusses his early inspirations, his early days at University and the Hughes Aircraft Company - a key time during this life. He also talks about  the cyclical nature of hypersonics research, and the challenges faced in computational fluid dynamics (CFD). Prof. Cummings emphasizes the importance of perseverance in engineering careers and the need for collaboration between experimental and computational methods. He also shares insights on the role of AI in hypersonics and offers valuable advice for aspiring engineers.

Prof. Russ Cummings graduated from California Polytechnic State University (Cal Poly) with a B.S. and M.S. in Aeronautical Engineering, before receiving his Ph.D. in Aerospace Engineering from the University of Southern California; he also received a B.A. in music from Cal Poly. He is currently Professor of Aeronautics at the U.S. Air Force Academy and Director of the Hypersonic Vehicle Simulation Institute. Prior to this he was Professor of Aerospace Engineering at Cal Poly, where he also served as department chairman for four years. He also worked at Hughes Aircraft Company, and completed a National Research Council postdoctoral research fellowship at NASA Ames Research Center, working on the computation of high angle-of-attack flowfields. He is a Fellow of the Royal Aeronautical Society and the American Institute of Aeronautics and Astronautics.

Distribution Statement A: approved for public release, PA# USAFA-DF-2025-652. The views expressed in this interview are those of the author and do not necessarily reflect the official policy or position of the United States Air Force Academy, the Air Force, the Department of Defense, or the U.S. Government.

Links

Aerodynamics for engineers: https://www.cambridge.org/us/universitypress/subjects/engineering/aerospace-engineering/aerodynamics-engineers-7th-edition?format=HB&isbn=9781009501309
RAeS Lanchester Named Lecture 2024: Frederick W. Lanchester and 'Aerodynamics' https://www.youtube.com/watch?app=desktop&v=lApNzYaZOmk&t=884s 
NASA at 50 (Prof Cummings is in the picture): https://images.nasa.gov/details/ARC-1989-AC89-0276-6 

Chapters

00:00 Introduction to the Podcast and Guest
04:56 Professor Russell Cummings: A Journey Through Engineering
31:14 The Evolution of Hypersonics Research
58:26 The Role of AI in Hypersonics and CFD
01:37:55 Advice for Aspiring Engineers