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.
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.
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.
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).