3 citations · 3 across the 10 of their papers we have counts for
11 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.…
Fourier Weak SINDy: Spectral Test Function Selection for Robust Model Identification
Zhiheng Chen, Urban Fasel, Anastasia Bizyaeva
We introduce Fourier Weak SINDy, a minimal noise-robust and interpretable derivative-free equation learning method that combines weak-form sparse equation learning with spectral de…
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…
Future Pathways for eVTOLs: A Design Optimization Perspective
Johannes Janning, Sophie F. Armanini, Urban Fasel
The rapid development of advanced urban air mobility, particularly electric vertical take-off and landing (eVTOL) aircraft, requires interdisciplinary approaches involving the futu…
Interpretable and Efficient Data-driven Discovery and Control of Distributed Systems
Florian Wolf, Nicolò Botteghi, Urban Fasel +1
Effectively controlling systems governed by Partial Differential Equations (PDEs) is crucial in several fields of Applied Sciences and Engineering. These systems usually yield sign…
Sparsifying Parametric Models with L0 Regularization
Nicolò Botteghi, Urban Fasel
This document contains an educational introduction to the problem of sparsifying parametric models with L0 regularization. We utilize this approach together with dictionary learnin…
Parametric PDE Control with Deep Reinforcement Learning and Differentiable L0-Sparse Polynomial Policies
Nicolò Botteghi, Urban Fasel
Optimal control of parametric partial differential equations (PDEs) is crucial in many applications in engineering and science. In recent years, the progress in scientific machine…