10 citations · 25 across the 7 of their papers we have counts for
4 papers · 1 filter
Bridging Data and Physics: A Graph Neural Network-Based Hybrid Twin Framework
M. Gorpinich, B. Moya, S. Rodriguez +6
Simulating complex unsteady physical phenomena relies on detailed mathematical models, simulated for instance by using the Finite Element Method (FEM). However, these models often…
Variational Rank Reduction Autoencoders for Generative Thermal Design
Alicia Tierz, Jad Mounayer, Beatriz Moya +1
Generative thermal design for complex geometries is fundamental in many areas of engineering, yet it faces two main challenges: the high computational cost of high-fidelity simulat…
Graph neural networks informed locally by thermodynamics
Alicia Tierz, Iciar Alfaro, David González +2
Thermodynamics-informed neural networks employ inductive biases for the enforcement of the first and second principles of thermodynamics. To construct these biases, a metriplectic…
A comparison of Single- and Double-generator formalisms for Thermodynamics-Informed Neural Networks
Pau Urdeitx, Icíar Alfaro, David González +2
The development of inductive biases has been shown to be a very effective way to increase the accuracy and robustness of neural networks, particularly when they are used to predict…