96 citations · 335 across the 12 of their papers we have counts for
13 papers
Port-metriplectic neural networks: thermodynamics-informed machine learning of complex physical systems
Quercus Hernández, Alberto Badías, Francisco Chinesta +1
We develop inductive biases for the machine learning of complex physical systems based on the port-Hamiltonian formalism. To satisfy by construction the principles of thermodynamic…
Thermodynamics-informed neural networks for physically realistic mixed reality
Quercus Hernández, Alberto Badías, Francisco Chinesta +1
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. In this conte…
Thermodynamics of learning physical phenomena
Elias Cueto, Francisco Chinesta
Thermodynamics could be seen as an expression of physics at a high epistemic level. As such, its potential as an inductive bias to help machine learning procedures attain accurate…
A Thermodynamics-informed Active Learning Approach to Perception and Reasoning about Fluids
Beatriz Moya, Alberto Badias, David Gonzalez +2
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…
Thermodynamics-informed graph neural networks
Quercus Hernández, Alberto Badías, Francisco Chinesta +1
In this paper we present a deep learning method to predict the temporal evolution of dissipative dynamic systems. We propose using both geometric and thermodynamic inductive biases…
Monitoring weeder robots and anticipating their functioning by using advanced topological data analysis
Tarek Frahi, Abel Sancarlos, Matthieu Galle +5
The present paper aims at analyzing the topological content of the complex trajectories that weeder-autonomous robots follow in operation. We will prove that the topological descri…