1 citations · 1 across the 5 of their papers we have counts for
5 papers
Multi-Fidelity SINDy: Sparse Discovery of Nonlinear Dynamical Systems with Fidelity-Weighted Measurements
Filippo Zacchei, Ana Larrañaga, Attilio Frangi +2
Data from simulations and experiments are rarely noise-free and often exhibit heterogeneous levels of fidelity. Measurement uncertainty may vary across repeated observations, sensi…
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…
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…
On latent dynamics learning in nonlinear reduced order modeling
Nicola Farenga, Stefania Fresca, Simone Brivio +1
In this work, we present the novel mathematical framework of latent dynamics models (LDMs) for reduced order modeling of parameterized nonlinear time-dependent PDEs. Our framework…
PTPI-DL-ROMs: pre-trained physics-informed deep learning-based reduced order models for nonlinear parametrized PDEs
Simone Brivio, Stefania Fresca, Andrea Manzoni
The coupling of Proper Orthogonal Decomposition (POD) and deep learning-based ROMs (DL-ROMs) has proved to be a successful strategy to construct non-intrusive, highly accurate, sur…