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05/22/2023
MMGP: a Mesh Morphing Gaussian Process-based machine learning method for regression of physical problems under non-parameterized geometrical variability
When learning simulations for modeling physical phenomena in industrial ...
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04/11/2023
A priori compression of convolutional neural networks for wave simulators
Convolutional neural networks are now seeing widespread use in a variety...
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10/26/2021
Uncertainty quantification in a mechanical submodel driven by a Wasserstein-GAN
The analysis of parametric and non-parametric uncertainties of very larg...
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08/27/2021
Optimal piecewise linear data compression for solutions of parametrized partial differential equations
Model order reduction has been extensively studied over the last two dec...
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08/09/2021
Uncertainty quantification for industrial design using dictionaries of reduced order models
We consider the dictionary-based ROM-net (Reduced Order Model) framework...
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03/25/2021
Physics-informed cluster analysis and a priori efficiency criterion for the construction of local reduced-order bases
Nonlinear model order reduction has opened the door to parameter optimiz...
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01/12/2021
