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 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.
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.
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.