no code implementations • 1 Apr 2024 • Pau Urdeitx, Icíar Alfaro, David González, Francisco Chinesta, Elías Cueto
The development of inductive biases has been shown to be a very effective way to increase the accuracy and robustness of neural networks, particularly when they are used to predict physical phenomena.
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 • 3 Nov 2022 • Quercus Hernández, Alberto Badías, Francisco Chinesta, Elías Cueto
We show that the constructed networks are able to learn the physics of complex systems by parts, thus alleviating the burden associated to the experimental characterization and posterior learning process of this kind of systems.
1 code implementation • 24 Oct 2022 • Quercus Hernández, Alberto Badías, Francisco Chinesta, Elías Cueto
The imminent impact of immersive technologies in society urges for active research in real-time and interactive physics simulation for virtual worlds to be realistic.
no code implementations • 26 Jul 2022 • Elias Cueto, Francisco Chinesta
Thermodynamics could be seen as an expression of physics at a high epistemic level.
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 • 3 Mar 2022 • Quercus Hernández, Alberto Badías, Francisco Chinesta, Elías Cueto
In this paper we present a deep learning method to predict the temporal evolution of dissipative dynamic systems.
no code implementations • 19 Aug 2021 • Tarek Frahi, Abel Sancarlos, Matthieu Galle, Xavier Beaulieu, Anne Chambard, Antonio Falco, Elias Cueto, Francisco Chinesta
The present paper aims at analyzing the topological content of the complex trajectories that weeder-autonomous robots follow in operation.
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 • 7 Jun 2021 • Abel Sancarlos, Morgan Cameron, Jean-Marc Le Peuvedic, Juliette Groulier, Jean-Louis Duval, Elias Cueto, Francisco Chinesta
The concept of Hybrid Twin (HT) has recently received a growing interest thanks to the availability of powerful machine learning techniques.
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).