S4 EP2 - Prof. Nathan Kutz on Physics-Informed AI and Data-Driven Modeling
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Physics-informed AI, DMD, SINDy and data-driven engineering are the focus of this conversation with Professor J. Nathan Kutz, Director of Physics-Informed AI at Autodesk. Neil and Nathan trace machine learning’s evolution in engineering, the role of physics in trustworthy models, and the future of autonomous agents, design automation and human expertise.
Full episode, corrected transcript and resources:
https://neilashton.co.uk/podcasts/s4-e2-prof-nathan-kutz-on-physics-informed-ai-and-data-driven-modeling/
Topics
History of machine learning in engineering
Dynamic Mode Decomposition (DMD) and Sparse Identification of Nonlinear Dynamics (SINDy)
Physics-informed AI and reduced-order modeling
The debate between physics-based and data-driven models
The future of autonomous agents and their impact on industry
Papers
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
A practical example of agentic AI for engineering design.
Links
Articraft project page
https://articraft3d.github.io/
Chapters
00:00 Podcast intro
00:40 Introduction to Episode
05:00 Welcoming Prof. Kutz
10:34 The Evolution of Data-Driven Modeling
16:13 Understanding the SINDy Algorithm and Its Implications
22:14 Comparing Reduced-Order Modeling and Modern Machine Learning
28:29 The Role of Data in Machine Learning and Physics
34:23 Challenges in Extrapolation and Real-World Applications
40:46 Insights from McLaren and Team Dynamics
46:07 The Shift from Academia to Industry
48:53 Collaboration and Innovation in Engineering
51:57 The Role of Human Expertise in Design
54:45 Leveraging AI in Formula One
57:32 The Future of AI and Workforce Dynamics
59:06 Navigating Career Choices in a Changing Landscape
01:03:02 The Evolution of Thought in Engineering
01:09:06 Preparing for the Future of Technology
01:14:04 Responsible Use of AI in Engineering
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