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09/14/2023
Nonlinear model order reduction for problems with microstructure using mesh informed neural networks
Many applications in computational physics involve approximating problem...
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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...
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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...
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07/04/2022
Approximation bounds for convolutional neural networks in operator learning
Recently, deep Convolutional Neural Networks (CNNs) have proven to be su...
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03/22/2022