Data-driven modeling and control via spectral submanifolds

Add to your list(s) Download to your calendar using vCal

• George Haller, ETH Zurich
• Friday 13 March 2026, 16:00-17:00
• MR2.

If you have a question about this talk, please contact Duncan Hewitt.

This talk has been canceled/deleted

I discuss a dynamical systems alternative to neural networks in the data-driven reduced-order modeling of nonlinear phenomena. Specifically, the recent concept of spectral submanifolds (SSMs) provides very low-dimensional attractors in virtually all dynamics problems of physical importance. A data-driven identification of the reduced dynamics on these SSMs gives a mathematically justified way to construct accurate and predictive reduced-order models for solids, fluids, and controls without the use of governing equations. I illustrate this on physical problems including structural vibrations, fluid-structure interactions, shear flows, plant dynamics, and the model-predictive control of soft robots.

This talk is part of the Fluid Mechanics (DAMTP) series.

This talk is included in these lists:

This talk is not included in any other list

Note that ex-directory lists are not shown.