14 citations · 14 across the 1 of their papers we have counts for
4 papers
PGD-based advanced nonlinear multiparametric regressions for constructing metamodels at the scarce-data limit
Abel Sancarlos, Victor Champaney, Jean-Louis Duval +2
Regressions created from experimental or simulated data enable the construction of metamodels, widely used in a variety of engineering applications. Many engineering problems invol…
Deep learning of thermodynamics-aware reduced-order models from data
Quercus Hernandez, Alberto Badias, David Gonzalez +2
We present an algorithm to learn the relevant latent variables of a large-scale discretized physical system and predict its time evolution using thermodynamically-consistent deep n…
Structure-preserving neural networks
Quercus Hernández, Alberto Badias, David Gonzalez +2
We develop a method to learn physical systems from data that employs feedforward neural networks and whose predictions comply with the first and second principles of thermodynamics…
Learning Physics from Data: a Thermodynamic Interpretation
Francisco Chinesta, Elias Cueto, Miroslav Grmela +3
Experimental data bases are typically very large and high dimensional. To learn from them requires to recognize important features (a pattern), often present at scales different to…