96 citations · 194 across the 5 of their papers we have counts for
5 papers
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…
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…
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…