1 code implementation • 9 Apr 2020 • Quercus Hernández, Alberto Badias, David Gonzalez, Francisco Chinesta, Elias Cueto
The method employs a minimum amount of data by enforcing the metriplectic structure of dissipative Hamiltonian systems in the form of the so-called General Equation for the Non-Equilibrium Reversible-Irreversible Coupling, GENERIC [M. Grmela and H. C Oettinger (1997).
1 code implementation • 3 Jul 2020 • Quercus Hernandez, Alberto Badias, David Gonzalez, Francisco Chinesta, Elias Cueto
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 neural networks.
no code implementations • 1 Sep 2020 • Alberto Badias, Iciar Alfaro, David Gonzalez, Francisco Chinesta, Elias Cueto
We propose a new methodology to estimate the 3D displacement field of deformable objects from video sequences using standard monocular cameras.
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.
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.