Outline
00:00 - Intro
01:30 - Origin story: Naples, electrical engineering, and the fascination with chaos
08:00 - What is chaos?
15:00 - DC-DC converters and discontinuity-induced bifurcations
22:00 - Piecewise-smooth dynamical systems
26:55 - Complex networks, synchronization, and pinning control
40:30- Synthetic biology: from gene regulatory networks to multicellular control
58:00 - COVID-19: a network epidemic model for Italy
1:02:00 - Multiscale control, statistical mechanics, and physics-informed control
1:19:10 - State of the field and the IEEE CSS
1:26:35 - Advice to young researchers
1:29:00 - Outro

Links
Mario's website: https://sites.google.com/site/dibernardogroup/home
Scuola Superiore Meridionale: https://www.ssm.unina.it/
Chaos by James Gleick: https://en.wikipedia.org/wiki/Chaos:_Making_a_New_Science
Control of chaos:https://en.wikipedia.org/wiki/Control_of_chaos
Erasmus programme: https://en.wikipedia.org/wiki/Erasmus_Programme
An Adaptive Approach to the Control and Synchronization of Continuous-time Chaotic Systems: https://doi.org/10.1142/S0218127496000254
Piecewise-smooth Dynamical Systems: Theory and Applications: https://doi.org/10.1007/978-1-84628-708-4
Bifurcations in nonsmooth dynamical systems: https://doi.org/10.1137/050625060 Controllability of complex networks via pinning:
https://doi.org/10.1103/PhysRevE.75.046103
Criteria for global pinning-controllability of complex networks: https://doi.org/10.1016/j.automatica.2008.07.007
Controllability of complex networks: https://doi.org/10.1038/nature10011
Controlling complex networks with complex nodes: https://doi.org/10.1038/s42254-023-00566-3
Analysis, design and implementation of a novel scheme for in-vivo control of synthetic gene regulatory networks: https://doi.org/10.1016/j.automatica.2011.01.073
In-vivo Real-time Control of Protein Expression from Endogenous and Synthetic Gene Networks: https://doi.org/10.1371/journal.pcbi.1003625
A network model of Italy shows that intermittent regional strategies can alleviate the COVID-19 epidemic: https://doi.org/10.1038/s41467-020-18827-5
A Continuification-Based Control Solution for Large-Scale Shepherding:
https://arxiv.org/abs/2411.04791
Shepherding control and herdability in complex multiagent systems: https://doi.org/10.1103/PhysRevResearch.6.L032012
Nonreciprocal field theory for decision-making in multi-agent control systems: https://doi.org/10.1038/s41467-025-63071-4

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Podcast info
Podcast website: https://www.incontrolpodcast.com/
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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.

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

Support the show

Podcast info
Podcast website: https://www.incontrolpodcast.com/
Apple Podcasts: https://tinyurl.com/5n84j85j
Spotify: https://tinyurl.com/4rwztj3c
RSS: https://tinyurl.com/yc2fcv4y
Youtube: https://tinyurl.com/bdbvhsj6
Facebook: https://tinyurl.com/3z24yr43
Twitter: https://twitter.com/IncontrolP
Instagram: https://tinyurl.com/35cu4kr4

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.

Outline

00:00 – Intro
04:43 – Life and background
08:45 – Bell Labs
13:42 – Inventing the negative feedback amplifier
18:15 – Nyquist's landmark contributions
20:43 – Regeneration theory
27:10 – Frequency response
32:03 – Cauchy’s argument principle
36:05 – The Nyquist criterion
41:37 – Why is it so hard?
45:27 – Robustness, margins, and practical aspects
56:41 – Beyond the Nyquist criterion
1:04:25 – Pitfalls and common misunderstandings
1:07:00 – Outro

Links

Brian Douglas's video: http://y2u.be/sof3meN96MA
The Idea Factory: https://en.wikipedia.org/wiki/The_Idea_Factory
Inventing the Negative Feedback Amplifier: https://doi.org/10.1109/MSPEC.1977.6501721
Johnson–Nyquist noise: https://doi.org/10.1103/PhysRev.32.110
Nyquist sampling theorem: https://en.wikipedia.org/wiki/Nyquist%E2%80%93Shannon_sampling_theorem
Regeneration theory: https://doi.org/10.1002/j.1538-7305.1932.tb02344.x
Gain and phase margins: https://en.wikipedia.org/wiki/Bode_plot#Gain_margin_and_phase_margin
Routh–Hurwitz criterion: https://en.wikipedia.org/wiki/Routh%E2%80%93Hurwitz_stability_criterion
Åström’s lecture: https://archive.control.lth.se/media/Staff/KarlJohanAstrom/Lectures/ASMENyquistLecture2005.pdf
Scale-Relative Graphs: https://doi.org/10.1109/TAC.2023.3234016

Support the show

Podcast info
Podcast website: https://www.incontrolpodcast.com/
Apple Podcasts: https://tinyurl.com/5n84j85j
Spotify: https://tinyurl.com/4rwztj3c
RSS: https://tinyurl.com/yc2fcv4y
Youtube: https://tinyurl.com/bdbvhsj6
Facebook: https://tinyurl.com/3z24yr43
Twitter: https://twitter.com/IncontrolP
Instagram: https://tinyurl.com/35cu4kr4

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.