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.
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 • 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.
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.