Stefania Fresca

Orcid: 0000-0001-8599-6588

According to our database1, Stefania Fresca authored at least 16 papers between 2020 and 2023.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of five.

Timeline

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Bibliography

2023
Uncertainty quantification for nonlinear solid mechanics using reduced order models with Gaussian process regression.
Comput. Math. Appl., November, 2023

Approximation bounds for convolutional neural networks in operator learning.
Neural Networks, April, 2023

Reduced Order Modeling of Nonlinear Vibrating Multiphysics Microstructures with Deep Learning-Based Approaches.
Sensors, March, 2023

Deep Learning-based surrogate models for parametrized PDEs: handling geometric variability through graph neural networks.
CoRR, 2023

Error estimates for POD-DL-ROMs: a deep learning framework for reduced order modeling of nonlinear parametrized PDEs enhanced by proper orthogonal decomposition.
CoRR, 2023

2022
Deep-HyROMnet: A Deep Learning-Based Operator Approximation for Hyper-Reduction of Nonlinear Parametrized PDEs.
J. Sci. Comput., 2022

Reduced order modeling of parametrized systems through autoencoders and SINDy approach: continuation of periodic solutions.
CoRR, 2022

Virtual twins of nonlinear vibrating multiphysics microstructures: physics-based versus deep learning-based approaches.
CoRR, 2022

Efficient approximation of cardiac mechanics through reduced order modeling with deep learning-based operator approximation.
CoRR, 2022

Long-time prediction of nonlinear parametrized dynamical systems by deep learning-based reduced order models.
CoRR, 2022

2021
A Comprehensive Deep Learning-Based Approach to Reduced Order Modeling of Nonlinear Time-Dependent Parametrized PDEs.
J. Sci. Comput., 2021

Deep learning-based reduced order models for the real-time simulation of the nonlinear dynamics of microstructures.
CoRR, 2021

Reduced order modeling of nonlinear microstructures through Proper Orthogonal Decomposition.
CoRR, 2021

Real-time simulation of parameter-dependent fluid flows through deep learning-based reduced order models.
CoRR, 2021

POD-DL-ROM: enhancing deep learning-based reduced order models for nonlinear parametrized PDEs by proper orthogonal decomposition.
CoRR, 2021

2020
Deep learning-based reduced order models in cardiac electrophysiology.
CoRR, 2020


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