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• W3000403725 startingPage "110444" @default.
• W3000403725 abstract "The least squares method with deep neural networks as function parametrization has been applied to solve certain high-dimensional partial differential equations (PDEs) successfully; however, its convergence is slow and might not be guaranteed even within a simple class of PDEs. To improve the convergence of the network-based least squares model, we introduce a novel self-paced learning framework, SelectNet, which quantifies the difficulty of training samples, treats samples equally in the early stage of training, and slowly explores more challenging samples, e.g., samples with larger residual errors, mimicking the human cognitive process for more efficient learning. In particular, a selection network and the PDE solution network are trained simultaneously; the selection network adaptively weighting the training samples of the solution network achieving the goal of self-paced learning. Numerical examples indicate that the proposed SelectNet model outperforms existing models on the convergence speed and the convergence robustness, especially for low-regularity solutions." @default.
• W3000403725 created "2020-01-23" @default.
• W3000403725 creator A5000567490 @default.
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• W3000403725 date "2021-09-01" @default.
• W3000403725 modified "2023-10-11" @default.
• W3000403725 title "SelectNet: Self-paced learning for high-dimensional partial differential equations" @default.
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• W3000403725 doi "https://doi.org/10.1016/j.jcp.2021.110444" @default.
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