96 citations · 335 across the 13 of their papers we have counts for
14 papers
A time multiscale decomposition in cyclic elasto-plasticity
Angelo Pasquale, Sebastian Rodriguez, Khanh Nguyen +2
For the numerical simulation of time-dependent problems, recent works suggest the use of a time marching scheme based on a tensorial decomposition of the time axis. This time-separ…
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…
Learning stable reduced-order models for hybrid twins
Abel Sancarlos, Morgan Cameron, Jean-Marc Le Peuvedic +4
The concept of Hybrid Twin (HT) has recently received a growing interest thanks to the availability of powerful machine learning techniques. This twin concept combines physics-base…
Physics perception in sloshing scenes with guaranteed thermodynamic consistency
Beatriz Moya, Alberto Badias, David Gonzalez +2
Physics perception very often faces the problem that only limited data or partial measurements on the scene are available. In this work, we propose a strategy to learn the full sta…
PGD-based advanced nonlinear multiparametric regressions for constructing metamodels at the scarce-data limit
Abel Sancarlos, Victor Champaney, Jean-Louis Duval +2
Regressions created from experimental or simulated data enable the construction of metamodels, widely used in a variety of engineering applications. Many engineering problems invol…
MORPH-DSLAM: Model Order Reduction for PHysics-based Deformable SLAM
Alberto Badias, Iciar Alfaro, David Gonzalez +2
We propose a new methodology to estimate the 3D displacement field of deformable objects from video sequences using standard monocular cameras. We solve in real time the complete (…
Deep learning of thermodynamics-aware reduced-order models from data
Quercus Hernandez, Alberto Badias, David Gonzalez +2
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 n…
Structure-preserving neural networks
Quercus Hernández, Alberto Badias, David Gonzalez +2
We develop a method to learn physical systems from data that employs feedforward neural networks and whose predictions comply with the first and second principles of thermodynamics…
Learning Physics from Data: a Thermodynamic Interpretation
Francisco Chinesta, Elias Cueto, Miroslav Grmela +3
Experimental data bases are typically very large and high dimensional. To learn from them requires to recognize important features (a pattern), often present at scales different to…