research
∙
09/14/2023
Nonlinear model order reduction for problems with microstructure using mesh informed neural networks
Many applications in computational physics involve approximating problem...
share
research
∙
09/01/2023
Multi-fidelity reduced-order surrogate modeling
High-fidelity numerical simulations of partial differential equations (P...
research
∙
08/07/2023
A staggered-in-time and non-conforming-in-space numerical framework for realistic cardiac electrophysiology outputs
Computer-based simulations of non-invasive cardiac electrical outputs, s...
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...
research
∙
08/02/2023
A digital twin framework for civil engineering structures
The digital twin concept represents an appealing opportunity to advance ...
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
∙
08/05/2022
Multi-fidelity surrogate modeling using long short-term memory networks
When evaluating quantities of interest that depend on the solutions to d...
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
∙
06/20/2022
A reduced order model for domain decompositions with non-conforming interfaces
In this paper we propose a reduced order modeling strategy for two-way D...
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
∙
03/22/2022
Learning Operators with Mesh-Informed Neural Networks
Thanks to their universal approximation properties and new efficient tra...
research
∙
03/17/2022
Efficient and certified solution of parametrized one-way coupled problems through DEIM-based data projection across non-conforming interfaces
One of the major challenges of coupled problems is to manage nonconformi...
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
∙
03/31/2021
Non intrusive reduced order modeling of parametrized PDEs by kernel POD and neural networks
We propose a nonlinear reduced basis method for the efficient approximat...
research
∙
03/26/2021
Online structural health monitoring by model order reduction and deep learning algorithms
Within a structural health monitoring (SHM) framework, we propose a simu...
research
∙
03/10/2021
A Deep Learning approach to Reduced Order Modelling of Parameter Dependent Partial Differential Equations
Within the framework of parameter dependent PDEs, we develop a construct...
research
∙
02/26/2021
Multi-fidelity regression using artificial neural networks: efficient approximation of parameter-dependent output quantities
Highly accurate numerical or physical experiments are often time-consumi...
research
∙
02/23/2021
Learning High-Order Interactions via Targeted Pattern Search
Logistic Regression (LR) is a widely used statistical method in empirica...
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
∙
01/09/2021
SUIHTER: A new mathematical model for COVID-19. Application to the analysis of the second epidemic outbreak in Italy
The COVID-19 epidemic is the last of a long list of pandemics that have ...
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
∙
02/12/2020
Fully convolutional networks for structural health monitoring through multivariate time series classification
We propose a novel approach to Structural Health Monitoring (SHM), aimin...
research
∙
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 ...
research
∙
01/09/2019