From the 2 of 10 linked papers with an AI index.
10 papers
Data-free neural PDE solvers based on Graph Neural Networks and weak forms
Mikel M. Iparraguirre, Iciar Alfaro, David Gonzalez +1
The paper introduces a neural network that solves partial differential equations without any training data by using a graph neural network and the weak form of the equations, compu…
Physics-informed, Generative Adversarial Design of Funicular Shells
Rúben Lourenço, IcÃar Alfaro, Beatriz Moya +1
The paper presents a physics‑informed generative adversarial network that creates three‑dimensional shell geometries optimized for pure compression (funicular shells), targeting 3D…
A Graph Neural Network approach to zero-shot Digital Twins
Alicia Tierz, IcÃar 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…
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…
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,…
On the under-reaching phenomenon in message-passing neural PDE solvers: revisiting the CFL condition
Lucas Tesan, Mikel M. Iparraguirre, David Gonzalez +2
This paper proposes sharp lower bounds for the number of message passing iterations required in graph neural networks (GNNs) when solving partial differential equations (PDE). This…