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Found 3 papers, 2 papers with code
Deep adaptive sampling for surrogate modeling without labeled data
In this work, we present a deep adaptive sampling method for surrogate modeling ($\text{DAS}^2$), where we generalize the deep adaptive sampling (DAS) method [62] [Tang, Wan and Yang, 2023] to build surrogate models for low-regularity parametric differential equations.
Bregman Graph Neural Network
Numerous recent research on graph neural networks (GNNs) has focused on formulating GNN architectures as an optimization problem with the smoothness assumption.
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Adversarial Adaptive Sampling: Unify PINN and Optimal Transport for the Approximation of PDEs
The key idea is to use a deep generative model to adjust random samples in the training set such that the residual induced by the approximate PDE solution can maintain a smooth profile when it is being minimized.
