4 papers · 1 filter
A Graph Neural Network approach to zero-shot Digital Twins
Alicia Tierz, Icíar Alfaro, David González +1
Traditional Predictive Digital Twins often remain geometrically rigid, requiring extensive retraining or fine-tuning whenever the underlying physical domain or boundary conditions…
MeshGraphNet-Transformer: Scalable Mesh-based Learned Simulation for Solid Mechanics
Mikel M. Iparraguirre, Iciar Alfaro, David Gonzalez +1
We present MeshGraphNet-Transformer (MGN-T), a novel architecture that combines the global modeling capabilities of Transformers with the geometric inductive bias of MeshGraphNets,…
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…