9 citations · 12 across the 7 of their papers we have counts for
8 papers
Physics-informed, Generative Adversarial Design of Funicular Shells
Rúben Lourenço, Icíar Alfaro, Beatriz Moya +1
Shell structures are pivotal in the fields of architecture and engineering, due to their aesthetic appeal and structural efficiency. Recently, 3D concrete printing has reignited th…
Variational Graph Neural Networks for Uncertainty Quantification in Inverse Problems
David Gonzalez, Alba Muixi, Beatriz Moya +1
The increasingly wide use of deep machine learning techniques in computational mechanics has significantly accelerated simulations of problems that were considered unapproachable j…
Resilience-based post disaster recovery optimization for infrastructure system via Deep Reinforcement Learning
Huangbin Liang, Beatriz Moya, Francisco Chinesta +1
Infrastructure systems are critical in modern communities but are highly susceptible to various natural and man-made disasters. Efficient post-disaster recovery requires repair-sch…
Thermodynamics-informed super-resolution of scarce temporal dynamics data
Carlos Bermejo-Barbanoj, Beatriz Moya, Alberto Badías +2
We present a method to increase the resolution of measurements of a physical system and subsequently predict its time evolution using thermodynamics-aware neural networks. Our meth…
A graph convolutional autoencoder approach to model order reduction for parametrized PDEs
Federico Pichi, Beatriz Moya, Jan S. Hesthaven
The present work proposes a framework for nonlinear model order reduction based on a Graph Convolutional Autoencoder (GCA-ROM). In the reduced order modeling (ROM) context, one is…
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…