GCA-ROM is a library which implements graph convolutional autoencoder architecture as a nonlinear model order reduction strategy.
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Updated
May 15, 2026 - Jupyter Notebook
GCA-ROM is a library which implements graph convolutional autoencoder architecture as a nonlinear model order reduction strategy.
Dimension reduced surrogate construction for parametric PDE maps
Derivative-Informed Neural Operator: An Efficient Framework for High-Dimensional Parametric Derivative Learning
Deep Adaptive Sampling for Surrogate Modeling Without Labeled Data
adaptive Stochastic Galerkin finite element methods for parametric PDEs
Graph Feedforward Networks: a resolution-invariant generalisation of feedforward networks for graphical data, applied to model order reduction
Learning-guided selection between full and limiting boundary laws in parametric PDEs
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