8 papers
HypEMBER: Hypernetwork-based Ensemble for Robust Policy Learning of Parametrized Dynamical Systems
Nicolò Botteghi, Gabriele Pascali, Urban Fasel +1
In this work we investigate reinforcement learning (RL) as a framework for the robust control of parametrized dynamical systems in presence of measurements and model uncertainties.…
Interconnection and Damping Assignment Passivity-Based Control using Sparse Neural ODEs
Nicolò Botteghi, Owen Brook, Urban Fasel +1
Interconnection and Damping Assignment Passivity-Based Control (IDA-PBC) is a nonlinear control technique that assigns a port-Hamiltonian (pH) structure to a controlled system usin…
HypeMARL: Multi-Agent Reinforcement Learning For High-Dimensional, Parametric, and Distributed Systems
Nicolò Botteghi, Matteo Tomasetto, Urban Fasel +2
Deep reinforcement learning has recently emerged as a promising feedback control strategy for complex dynamical systems governed by partial differential equations (PDEs). When deal…
Sparse Identification of Nonlinear Dynamics with Conformal Prediction
Urban Fasel
The Sparse Identification of Nonlinear Dynamics (SINDy) is a method for discovering nonlinear dynamical system models from data. Quantifying uncertainty in SINDy models is essentia…
SINDy on slow manifolds
Diemen Delgado-Cano, Erick Kracht, Urban Fasel +1
The sparse identification of nonlinear dynamics (SINDy) has been established as an effective method to learn interpretable models of dynamical systems from data. However, for high-…
Interpretable low-order representation of eigenmode deformation in parameterized dynamical systems
Nicolas Torres-Ulloa, Erick Kracht, Urban Fasel +1
Modal analysis has long been consolidated as a basic tool to interpret dynamics and build low-order models of mechanical, thermal, and fluid systems. Eigenmodes arising from the sp…