Stefania Fresca | DeepAI

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∙ 08/03/2023

Deep Learning-based surrogate models for parametrized PDEs: handling geometric variability through graph neural networks

Mesh-based simulations play a key role when modeling complex physical sy...

0 Nicola Rares Franco, et al. ∙

∙ 05/08/2023

Error estimates for POD-DL-ROMs: a deep learning framework for reduced order modeling of nonlinear parametrized PDEs enhanced by proper orthogonal decomposition

POD-DL-ROMs have been recently proposed as an extremely versatile strate...

0 Simone Brivio, et al. ∙

∙ 02/16/2023

Uncertainty quantification for nonlinear solid mechanics using reduced order models with Gaussian process regression

Uncertainty quantification (UQ) tasks, such as sensitivity analysis and ...

0 Ludovica Cicci, et al. ∙

∙ 11/13/2022

Reduced order modeling of parametrized systems through autoencoders and SINDy approach: continuation of periodic solutions

Highly accurate simulations of complex phenomena governed by partial dif...

0 Paolo Conti, et al. ∙

∙ 07/04/2022

Approximation bounds for convolutional neural networks in operator learning

Recently, deep Convolutional Neural Networks (CNNs) have proven to be su...

0 Nicola Rares Franco, et al. ∙

∙ 05/12/2022

Virtual twins of nonlinear vibrating multiphysics microstructures: physics-based versus deep learning-based approaches

Micro-Electro-Mechanical-Systems are complex structures, often involving...

0 Giorgio Gobat, et al. ∙

∙ 02/08/2022

Efficient approximation of cardiac mechanics through reduced order modeling with deep learning-based operator approximation

Reducing the computational time required by high-fidelity, full order mo...

0 Ludovica Cicci, et al. ∙

∙ 02/05/2022

Deep-HyROMnet: A deep learning-based operator approximation for hyper-reduction of nonlinear parametrized PDEs

To speed-up the solution to parametrized differential problems, reduced ...

0 Ludovica Cicci, et al. ∙

∙ 01/25/2022

Long-time prediction of nonlinear parametrized dynamical systems by deep learning-based reduced order models

Deep learning-based reduced order models (DL-ROMs) have been recently pr...

0 Federico Fatone, et al. ∙

∙ 11/24/2021

Deep learning-based reduced order models for the real-time simulation of the nonlinear dynamics of microstructures

We propose a non-intrusive Deep Learning-based Reduced Order Model (DL-R...

0 Stefania Fresca, et al. ∙

∙ 06/10/2021

Real-time simulation of parameter-dependent fluid flows through deep learning-based reduced order models

Simulating fluid flows in different virtual scenarios is of key importan...

0 Stefania Fresca, et al. ∙

∙ 01/28/2021

POD-DL-ROM: enhancing deep learning-based reduced order models for nonlinear parametrized PDEs by proper orthogonal decomposition

Deep learning-based reduced order models (DL-ROMs) have been recently pr...

17 Stefania Fresca, et al. ∙

∙ 06/02/2020

Deep learning-based reduced order models in cardiac electrophysiology

Predicting the electrical behavior of the heart, from the cellular scale...

0 Stefania Fresca, et al. ∙

∙ 01/12/2020

A comprehensive deep learning-based approach to reduced order modeling of nonlinear time-dependent parametrized PDEs

Traditional reduced order modeling techniques such as the reduced basis ...

0 Stefania Fresca, et al. ∙

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