170 citations · 176 across the 13 of their papers we have counts for
13 papers
Handling geometrical variability in nonlinear reduced order modeling through Continuous Geometry-Aware DL-ROMs
Simone Brivio, Stefania Fresca, Andrea Manzoni
Deep Learning-based Reduced Order Models (DL-ROMs) provide nowadays a well-established class of accurate surrogate models for complex physical systems described by parametrized PDE…
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…
An optimal control strategy to design passive thermal cloaks of arbitrary shape
Riccardo Saporiti, Carlo Sinigaglia, Andrea Manzoni +1
In this paper we describe a numerical framework for achieving passive thermal cloaking of arbitrary shapes in both static and transient regimes. The design strategy is cast as the…
SINDy vs Hard Nonlinearities and Hidden Dynamics: a Benchmarking Study
Aurelio Raffa Ugolini, Valentina Breschi, Andrea Manzoni +1
In this work we analyze the effectiveness of the Sparse Identification of Nonlinear Dynamics (SINDy) technique on three benchmark datasets for nonlinear identification, to provide…
On the latent dimension of deep autoencoders for reduced order modeling of PDEs parametrized by random fields
Nicola Rares Franco, Daniel Fraulin, Andrea Manzoni +1
Deep Learning is having a remarkable impact on the design of Reduced Order Models (ROMs) for Partial Differential Equations (PDEs), where it is exploited as a powerful tool for tac…
Nonlinear model order reduction for problems with microstructure using mesh informed neural networks
Piermario Vitullo, Alessio Colombo, Nicola Rares Franco +2
Many applications in computational physics involve approximating problems with microstructure, characterized by multiple spatial scales in their data. However, these numerical solu…