6 papers
Deep Invertible Autoencoders for Dimensionality Reduction of Dynamical Systems
Nicolò Botteghi, Silke Glas, Christoph Brune
Constructing reduced-order models (ROMs) capable of efficiently predicting the evolution of high-dimensional, parametric systems is crucial in many applications in engineering and…
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…
HypeRL: Hypernetwork-Based Reinforcement Learning for Control of Parametrized Dynamical Systems
Nicolò Botteghi, Stefania Fresca, Mengwu Guo +1
In this work, we devise a new, general-purpose reinforcement learning strategy for the optimal control of parametric dynamical systems. Such problems frequently arise in applied sc…
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…