Amortizing Physics-Informed Neural Solvers via Graph Hypernetworks
ScientificML
Proposes a graph hypernetwork approach using an operator graph that explicitly represents PDE fields, derivatives, terms, and residuals as nodes, enabling amortized neural solver meta-training across related partial differential equations.
See LICENSE file Physics-Informed Neural NetworksGraph HypernetworksOperator Graph