Beatriz Moya

According to our database, Beatriz Moya authored at least 14 papers between 2020 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

Physics-informed, Generative Adversarial Design of Funicular Shells.

Rúben Lourenço, Icíar Alfaro, Beatriz Moya, Elías Cueto

CoRR, April, 2026

Variational Graph Neural Networks for Uncertainty Quantification in Inverse Problems.

David González, Alba Muixi, Beatriz Moya, Elías Cueto

CoRR, March, 2026

Stress-constrained Topology Optimization for Metamaterial Microstructure Design.

Yanda Chen, Sebastian Rodriguez, Beatriz Moya, Francisco Chinesta

CoRR, February, 2026

Quantifying the value of seismic structural health monitoring for post-earthquake recovery of electric power system in terms of resilience enhancement.

Huangbin Liang, Beatriz Moya, Francisco Chinesta, Eleni N. Chatzi

Reliab. Eng. Syst. Saf., 2026

A multi-model probabilistic framework for seismic risk assessment and retrofit planning of electric power networks.

Huangbin Liang, Beatriz Moya, Francisco Chinesta, Eleni N. Chatzi

Reliab. Eng. Syst. Saf., 2026

Resilience-based post disaster recovery optimization for infrastructure system via deep reinforcement learning.

Huangbin Liang, Beatriz Moya, Francisco Chinesta, Eleni N. Chatzi

Reliab. Eng. Syst. Saf., 2026

2025

Bridging Data and Physics: A Graph Neural Network-Based Hybrid Twin Framework.

M. Gorpinich, Beatriz Moya, Sebastian Rodriguez, F. Meraghni, Y. Jaafra, A. Briot, M. Henner, R. Leon, Francisco Chinesta

CoRR, December, 2025

Application of Reduced-Order Models for Temporal Multiscale Representations in the Prediction of Dynamical Systems.

Elias Al Ghazal, Jad Mounayer, Beatriz Moya, Sebastian Rodriguez, Chady Ghnatios, Francisco Chinesta

CoRR, October, 2025

Variational Rank Reduction Autoencoders for Generative Thermal Design.

Alicia Tierz, Jad Mounayer, Beatriz Moya, Francisco Chinesta

CoRR, September, 2025

2024

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

Federico Pichi, Beatriz Moya, Jan S. Hesthaven

J. Comput. Phys., March, 2024

Thermodynamics-informed super-resolution of scarce temporal dynamics data.

Carlos Bermejo-Barbanoj, Beatriz Moya, Alberto Badías, Francisco Chinesta, Elías Cueto

CoRR, 2024

2023

Physics Perception in Sloshing Scenes With Guaranteed Thermodynamic Consistency.

Beatriz Moya, Alberto Badías, David González, Francisco Chinesta, Elías Cueto

IEEE Trans. Pattern Anal. Mach. Intell., 2023

2022

Physics-informed Reinforcement Learning for Perception and Reasoning about Fluids.

Beatriz Moya, Alberto Badías, David González, Francisco Chinesta, Elías Cueto

CoRR, 2022

2020

Learning Physics from Data: A Thermodynamic Interpretation.

Francisco Chinesta, Elías Cueto, Miroslav Grmela, Beatriz Moya, Michal Pavelka, Martin Sípka

Proceedings of the Geometric Structures of Statistical Physics, Information Geometry, and Learning, 2020