77 citations · 197 across the 20 of their papers we have counts for
20 papers
Correcting Neural Operator Spectral Bias via Diffusion Posterior Sampling with Sparse Observations
Niccolò Perrone, Fanny Lehmann, Stefania Fresca +1
Neural operator surrogates (NO) approximate PDE solutions orders of magnitude faster than numerical solvers, but suffer from spectral bias: high-frequency content is systematically…
Explainable Deep Learning-based Classification of Wolff-Parkinson-White Electrocardiographic Signals
Alice Ragonesi, Stefania Fresca, Karli Gillette +3
Wolff-Parkinson-White (WPW) syndrome is a cardiac electrophysiology (EP) disorder caused by the presence of an accessory pathway (AP) that bypasses the atrioventricular node, faste…
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…
Deep Learning-based surrogate models for parametrized PDEs: handling geometric variability through graph neural networks
Nicola Rares Franco, Stefania Fresca, Filippo Tombari +1
Mesh-based simulations play a key role when modeling complex physical systems that, in many disciplines across science and engineering, require the solution of parametrized time-de…
Error estimates for POD-DL-ROMs: a deep learning framework for reduced order modeling of nonlinear parametrized PDEs enhanced by proper orthogonal decomposition
Simone Brivio, Stefania Fresca, Nicola Rares Franco +1
POD-DL-ROMs have been recently proposed as an extremely versatile strategy to build accurate and reliable reduced order models (ROMs) for nonlinear parametrized partial differentia…
Uncertainty quantification for nonlinear solid mechanics using reduced order models with Gaussian process regression
Ludovica Cicci, Stefania Fresca, Mengwu Guo +2
Uncertainty quantification (UQ) tasks, such as sensitivity analysis and parameter estimation, entail a huge computational complexity when dealing with input-output maps involving t…
Reduced order modeling of parametrized systems through autoencoders and SINDy approach: continuation of periodic solutions
Paolo Conti, Giorgio Gobat, Stefania Fresca +2
Highly accurate simulations of complex phenomena governed by partial differential equations (PDEs) typically require intrusive methods and entail expensive computational costs, whi…
Approximation bounds for convolutional neural networks in operator learning
Nicola Rares Franco, Stefania Fresca, Andrea Manzoni +1
Recently, deep Convolutional Neural Networks (CNNs) have proven to be successful when employed in areas such as reduced order modeling of parametrized PDEs. Despite their accuracy…
Virtual twins of nonlinear vibrating multiphysics microstructures: physics-based versus deep learning-based approaches
Giorgio Gobat, Stefania Fresca, Andrea Manzoni +1
Micro-Electro-Mechanical-Systems are complex structures, often involving nonlinearites of geometric and multiphysics nature, that are used as sensors and actuators in countless app…
Efficient approximation of cardiac mechanics through reduced order modeling with deep learning-based operator approximation
Ludovica Cicci, Stefania Fresca, Andrea Manzoni +1
Reducing the computational time required by high-fidelity, full order models (FOMs) for the solution of problems in cardiac mechanics is crucial to allow the translation of patient…
Deep-HyROMnet: A deep learning-based operator approximation for hyper-reduction of nonlinear parametrized PDEs
Ludovica Cicci, Stefania Fresca, Andrea Manzoni
To speed-up the solution to parametrized differential problems, reduced order models (ROMs) have been developed over the years, including projection-based ROMs such as the reduced-…
Long-time prediction of nonlinear parametrized dynamical systems by deep learning-based reduced order models
Federico Fatone, Stefania Fresca, Andrea Manzoni
Deep learning-based reduced order models (DL-ROMs) have been recently proposed to overcome common limitations shared by conventional ROMs - built, e.g., exclusively through proper…
Deep learning-based reduced order models for the real-time simulation of the nonlinear dynamics of microstructures
Stefania Fresca, Giorgio Gobat, Patrick Fedeli +2
We propose a non-intrusive Deep Learning-based Reduced Order Model (DL-ROM) capable of capturing the complex dynamics of mechanical systems showing inertia and geometric nonlineari…
Real-time simulation of parameter-dependent fluid flows through deep learning-based reduced order models
Stefania Fresca, Andrea Manzoni
Simulating fluid flows in different virtual scenarios is of key importance in engineering applications. However, high-fidelity, full-order models relying, e.g., on the finite eleme…
POD-DL-ROM: enhancing deep learning-based reduced order models for nonlinear parametrized PDEs by proper orthogonal decomposition
Stefania Fresca, Andrea Manzoni
Deep learning-based reduced order models (DL-ROMs) have been recently proposed to overcome common limitations shared by conventional reduced order models (ROMs) - built, e.g., thro…
Deep learning-based reduced order models in cardiac electrophysiology
Stefania Fresca, Andrea Manzoni, Luca Dedè +1
Predicting the electrical behavior of the heart, from the cellular scale to the tissue level, relies on the formulation and numerical approximation of coupled nonlinear dynamical s…
A comprehensive deep learning-based approach to reduced order modeling of nonlinear time-dependent parametrized PDEs
Stefania Fresca, Luca Dede, Andrea Manzoni
Traditional reduced order modeling techniques such as the reduced basis (RB) method (relying, e.g., on proper orthogonal decomposition (POD)) suffer from severe limitations when de…