Learning & Dynamics Seminars
Together with our FEMTO colleague Prof. John Dudley, Professor of Optical Physics, the FEMTO Neuro group is putting together a monthy series of invited seminars on the topic of machine learning for dynamical systems.
The “Learning & Dynamics” Seminar Series wishes to address - roughly defined - how one may analyse & discover physically-interpretable models of dynamical systems on the basis of observed temporal data, rather than attempt to study them in a purely analytical manner. Our seminar aims to address interdisciplinary applications across complex physical or physiological systems, such as neuroscience, non-linear optics or fluid mechanics.
For our inaugural 2024-2025 series, we have assembled a program with a fantastic list of early-career EU researchers who have made recent notable contributions to this emerging field. As a kick-off event on Tuesday 17th 2024, we are also honored to host an invited talk by Prof. J. Nathan Kutz, Director of the NSF AI Institute in Dynamic Systems at the University of Washington, USA. Title, abtracts and speaker bios come below.
2024-2025 Program
| Date | Hour | Speaker | Title | Location |
|---|---|---|---|---|
| Tue. Sept 17, 2024 (Note unusual day) | 2pm | Prof. J. Nathan Kutz (Dept. Applied Mathematics, University of Washington) | Modern Sensing and Learning with Machine Learning | Amphi JJ. Gagnepain |
| Thu. Oct 17, 2024 | 2pm | Dr Nicolas Boullé (Dept. Applied Mathematics, Imperial College London) | An overview of operator learning | Amphi J. Haag |
| Thu. Nov 21, 2024 | 2pm | Dr Lou Zonca (Center for Brain & Cognition, University Pompeu Fabra, Barcelona) | tba | Amphi JJ. Gagnepain |
| Thu. Dec 19, 2024 | 2pm | Dr Emmanuel De Bezenac (Dept of Mathematics, UTH Zurich) | tba | Amphi J. Haag |
| Thu. Jan 17, 2025 | 2pm | Dr Zahra Monfared (Interdisciplinary Center for Scientific Computing, Heidelberg University) | tba | Amphi JJ Gagnepain |
| Thu. Feb 20, 2025 | 2pm | Dr Karim Cherifi (Centre for Industry and Science, TU Berlin) | tba | Amphi J. Haag |
| Thu. Mar 20, 2025 | 2pm | Dr Lorenzo Fontolan (Turing Center for Living Systems, Aix-Marseille University) | tba | Amphi JJ. Gagnepain |
| Thu. Apr 17, 2025 | 2pm | Dr Richard Gao (Tübingen AI Center, University of Tübingen) | tba | Amphi J. Haag |
| Thu. May 15, 2025 | 2pm | tba | tba | Amphi J. Haag |
| Thu. June 19, 2025 | 2pm | tba | tba | Amphi J. Haag |
Time & Place
L&D seminars are held in-person, monthly (typically the 3rd Thursday of the month, 2pm), and alternate every other month between 2 locations in the FEMTO premises in Besançon, France (Amphithéatre Jean-Jacques Gagnepain, FEMTO TEMIS Building, 15B avenue des Montboucons & Amphithéatre Jules Haag, FEMTO SUPMICROTECH Building, 26 rue de l’Epitaphe).
Attendance is free, in the limit of available seats (no reservation taken). For non-FEMTO personnel, please be prepared to identify yourself with a mandatory ID document at the entrance counter in each location.
Information, contact: JJ Aucouturier, FEMTO Neuro Group
Title and abstracts
Inaugural Seminar: Tue. Sept. 17th, 2024
Speaker: Prof. Nathan Kutz, University of Washington
Title: Modern Sensing and Learning with Machine Learning
Abstract: Sensing is a universal task in science and engineering. Downstream tasks from sensing include learning dynamical models, inferring full state estimates of a system (system identification), control decisions, and forecasting. These tasks are exceptionally challenging to achieve with limited sensors, noisy measurements, and corrupt or missing data. Existing techniques typically use current (static) sensor measurements to perform such tasks and require principled sensor placement or an abundance of randomly placed sensors. In contrast, we propose a SHallow REcurrent Decoder (SHRED) neural network structure which incorporates (i) a recurrent neural network (LSTM) to learn a latent representation of the temporal dynamics of the sensors, and (ii) a shallow decoder that learns a mapping between this latent representation and the high-dimensional state space. By explicitly accounting for the time-history, or trajectory, of the sensor measurements, SHRED enables accurate reconstructions with far fewer sensors, outperforms existing techniques when more measurements are available, and is agnostic towards sensor placement. In addition, a compressed representation of the high-dimensional state is directly obtained from sensor measurements, which provides an on-the-fly compression for modeling physical and engineering systems. Forecasting is also achieved from the sensor time-series data alone, producing an efficient paradigm for predicting temporal evolution with an exceptionally limited number of sensors.
Bio: Nathan Kutz is the Yasuko Endo and Robert Bolles Professor of Applied Mathematics and Electrical and Computer Engineering and Director of the AI Institute in Dynamic Systems at the University of Washington, having served as chair of applied mathematics from 2007-2015. He received the BS degree in physics and mathematics from the University of Washington in 1990 and the Phd in applied mathematics from Northwestern University in 1994. He was a postdoc in the applied and computational mathematics program at Princeton University before taking his faculty position. He has a wide range of interests, including neuroscience to fluid dynamics where he integrates machine learning with dynamical systems and control.
Web: http://faculty.washington.edu/kutz/
October Seminar: Thur. Oct. 17th, 2024
Speaker: Dr. Nicolas Boullé, Imperial College London
Title: An overview of operator learning
Abstract: Operator learning is an emerging field at the intersection of machine learning, physics, and mathematics, that aims to discover properties of unknown physical systems from experimental data. Popular techniques exploit the approximation power of deep learning to learn solution operators, which map source terms to solutions of the underlying PDE. Solution operators can then produce surrogate data for data-intensive machine learning approaches such as learning reduced order models for design optimization in engineering and PDE recovery. In this talk, we will provide a brief overview of the growing field of operator learning and see how numerical linear algebra algorithms, such as the randomized singular value decomposition, can be exploited to gain theoretical and mechanistic understanding of operator learning architectures.
Bio: Nicolas Boullé is an Assistant Professor in Applied Mathematics at Imperial College London. He obtained a PhD in numerical analysis at the University of Oxford in 2022 and was a postdoc at the University of Cambridge from 2022-2024. His research focuses on the intersection between numerical analysis and deep learning, with a specific emphasis on learning physical models from data, particularly in the context of partial differential equations learning. He was awarded a Leslie Fox Prize in 2021 and a SIAM Best Paper Prize in Linear Algebra in 2024 for his work on operator learning .
Web: https://nboulle.github.io
Acknowledgements
The L&D seminar series is funded by generous support from Région Bourgogne Franche-Comté (ANER ASPECT, 2022-2025, PI JJ Aucouturier).