research
∙
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...
share
research
∙
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...
research
∙
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 ...
research
∙
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...
research
∙
07/04/2022
Approximation bounds for convolutional neural networks in operator learning
Recently, deep Convolutional Neural Networks (CNNs) have proven to be su...
research
∙
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...
research
∙
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...
research
∙
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 ...
research
∙
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...
research
∙
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...
research
∙
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...
research
∙
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...
research
∙
06/02/2020
Deep learning-based reduced order models in cardiac electrophysiology
Predicting the electrical behavior of the heart, from the cellular scale...
research
∙
01/12/2020