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• W4290804306 abstract "Abstract Partial differential equations (PDEs) are the most ubiquitous tools for modeling natural science problems and have long received attention. Physics‐informed neural networks (PINNs) are emerging approaches to approximately solve PDEs. PINNs use automatic differentiation technology to construct the residual of PDEs in the loss function to encode physics conservation laws. We call this process the Single‐Net strategy. Due to the dependency of automatic differentiation among different orders of derivatives, the efficiency of PINNs under the Single‐Net strategy is limited. To address this issue, we propose the Multi‐Net strategy to decouple the dependency. Compared with the Single‐Net strategy, the Multi‐Net strategy reduces the training time of PINNs, and meanwhile, keeps the prediction accuracy. The effectiveness of the proposed strategy is demonstrated through time complexity analysis and a collection of experiments on Burgers equation, advection‐dispersion equation, Kdv equation, and Allen–Cahn equation." @default.
• W4290804306 created "2022-08-12" @default.
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• W4290804306 date "2022-08-11" @default.
• W4290804306 modified "2023-09-26" @default.
• W4290804306 title "Multi‐Net strategy: Accelerating physics‐informed neural networks for solving partial differential equations" @default.
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• W4290804306 doi "https://doi.org/10.1002/spe.3136" @default.
• W4290804306 hasPublicationYear "2022" @default.
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