no code implementations • 27 Feb 2024 • Carlos Bermejo-Barbanoj, Beatriz Moya, Alberto Badías, Francisco Chinesta, Elías Cueto
Then, a second neural network is trained to learn the physical structure of the latent variables and predict their temporal evolution.
1 code implementation • 15 May 2023 • Federico Pichi, Beatriz Moya, Jan S. Hesthaven
Here, we develop a non-intrusive and data-driven nonlinear reduction approach, exploiting GNNs to encode the reduced manifold and enable fast evaluations of parametrized PDEs.
1 code implementation • 11 Mar 2022 • Beatriz Moya, Alberto Badias, David Gonzalez, Francisco Chinesta, Elias Cueto
Learning and reasoning about physical phenomena is still a challenge in robotics development, and computational sciences play a capital role in the search for accurate methods able to provide explanations for past events and rigorous forecasts of future situations.
no code implementations • 24 Jun 2021 • Beatriz Moya, Alberto Badias, David Gonzalez, Francisco Chinesta, Elias Cueto
Physics perception very often faces the problem that only limited data or partial measurements on the scene are available.