Search Results for author: Beatriz Moya

Found 4 papers, 2 papers with code

Thermodynamics-informed super-resolution of scarce temporal dynamics data

no code implementations27 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.

Super-Resolution

A graph convolutional autoencoder approach to model order reduction for parametrized PDEs

1 code implementation15 May 2023 Federico Pichi, Beatriz Moya, Jan S. Hesthaven

Here, we develop a non-intrusive and data-driven nonlinear reduction approach, exploiting GNNs to encode the reduced manifold and enable fast evaluations of parametrized PDEs.

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

1 code implementation11 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.

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