Search Results for author: Elias Cueto

Found 8 papers, 2 papers with code

Thermodynamics of learning physical phenomena

no code implementations26 Jul 2022 Elias Cueto, Francisco Chinesta

Thermodynamics could be seen as an expression of physics at a high epistemic level.

Inductive Bias

A Thermodynamics-informed Active Learning Approach to Perception and Reasoning about Fluids

no code implementations11 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.

Active Learning

Physics perception in sloshing scenes with guaranteed thermodynamic consistency

no code implementations24 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.

Learning stable reduced-order models for hybrid twins

no code implementations7 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.

MORPH-DSLAM: Model Order Reduction for PHysics-based Deformable SLAM

no code implementations1 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.

Deep learning of thermodynamics-aware reduced-order models from data

1 code implementation3 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.

Total Energy

Structure-preserving neural networks

1 code implementation9 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).

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