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- W4307482007 abstract "Neural network-based methods for solving differential equations have been gaining traction. They work by improving the differential equation residuals of a neural network on a sample of points in each iteration. However, most of them employ standard sampling schemes like uniform or perturbing equally spaced points. We present a novel sampling scheme which samples points adversarially to maximize the loss of the current solution estimate. A sampler architecture is described along with the loss terms used for training. Finally, we demonstrate that this scheme outperforms pre-existing schemes by comparing both on a number of problems." @default.
- W4307482007 created "2022-11-02" @default.
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- W4307482007 date "2021-11-20" @default.
- W4307482007 modified "2023-09-28" @default.
- W4307482007 title "Adversarial Sampling for Solving Differential Equations with Neural Networks" @default.
- W4307482007 doi "https://doi.org/10.48550/arxiv.2111.12024" @default.
- W4307482007 hasPublicationYear "2021" @default.
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