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cs.LG2025
Equilibrium Conserving Neural Operators for Super-Resolution Learning
Vivek Oommen, Andreas E. Robertson, Daniel Diaz +5
Neural surrogate solvers can estimate solutions to partial differential equations in physical problems more efficiently than standard numerical methods, but require extensive high-…
cs.LG2021
Mesh-based graph convolutional neural networks for modeling materials with microstructure
Ari Frankel, Cosmin Safta, Coleman Alleman +1
Predicting the evolution of a representative sample of a material with microstructure is a fundamental problem in homogenization. In this work we propose a graph convolutional neur…