Outline 00:00 - Intro 01:15 - Origin story: early path and the road to science 04:20 - On graphical visualization and aphantasia 08:08 - The interest in fluid dynamics 12:00 - Caltech, Jerry Marsden, and the move to the Pacific time zone 19:43 - Dynamic Mode Decomposition (DMD) and the Koopman operator 27:15 - On teaching and the Eigensteve channel 39:22 - SINDy: Sparse Identification of Nonlinear Dynamics 45:45 - Automatic knowledge creation and Explainable AI 54:31 - HydroGym: RL benchmarks for fluid flow control 1:01:37 - Optimization boot camp 1:05:31 - Collimator 1:13:18 - Outro
Links Steve's website: https://www.eigensteve.com/ Eigensteve channel: https://www.youtube.com/c/eigensteve Jerrold E. Marsden: https://en.wikipedia.org/wiki/Jerrold_E._Marsden Aphantasia: https://en.wikipedia.org/wiki/Aphantasia J. Nathan Kutz: https://amath.washington.edu/people/j-nathan-kutz Clarence W. Rowley: https://cwrowley.princeton.edu/ DMD: https://en.wikipedia.org/wiki/Dynamic_mode_decomposition Koopman operator: https://en.wikipedia.org/wiki/Koopman_operator Dynamic Mode Decomposition book: https://epubs.siam.org/doi/book/10.1137/1.9781611974508 On Dynamic Mode Decomposition paper: https://doi.org/10.3934/jcd.2014.1.391 DMD with control: https://arxiv.org/abs/1409.6358 Compressed sensing and DMD: https://doi.org/10.3934/jcd.2015002 Modern Koopman Theory for Dynamical Systems: https://arxiv.org/abs/2102.12086 Deep learning for universal linear embeddings of nonlinear dynamics: https://doi.org/10.1038/s41467-018-07210-0 Data-driven discovery of Koopman eigenfunctions for control: https://doi.org/10.1088/2632-2153/abf0f5 PyDMD: https://github.com/PyDMD Discovering governing equations from data by sparse identification of nonlinear dynamical systems: https://doi.org/10.1073/pnas.1517384113 Data-driven discovery of partial differential equations: https://doi.org/10.1126/sciadv.1602614 SINDy for model predictive control in the low-data limit: https://doi.org/10.1098/rspa.2018.0335 PySINDy: https://github.com/dynamicslab/pysindy SINDy with control: https://arxiv.org/abs/2108.13404 SINDy review: https://doi.org/10.1146/annurev-control-030123-015238 Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control: http://www.databookuw.com Explainable AI: Learning from the Learners: https://arxiv.org/abs/2601.05525 HydroGym: https://github.com/dynamicslab/hydrogym
Acknowledgments and sponsors This episode was supported by the National Centre of Competence in Research on «Dependable, ubiquitous automation» and the IFAC Activity fund. The podcast benefits from the help of an incredibly talented and passionate team. Special thanks to L. Seward, E. Cahard, F. Banis, F. Dörfler, J. Lygeros, ETH studio and mirrorlake . Music was composed by A New Element.
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