9 citations · 9 across the 11 of their papers we have counts for
5 papers · 1 filter
Thermodynamics-informed graph neural networks for real-time simulation of digital human twins
Lucas Tesán, David González, Pedro Martins +1
The growing importance of real-time simulation in the medical field has exposed the limitations and bottlenecks inherent in the digital representation of complex biological systems…
On the feasibility of foundational models for the simulation of physical phenomena
Alicia Tierz, Mikel M. Iparraguirre, Iciar Alfaro +3
We explore the feasibility of foundation models for the simulation of physical phenomena, with emphasis on continuum (solid and fluid) mechanics. Although so-called learned 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…
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…