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About Learning To Solve Multiple Partial Differential Equations With DNN
This document describes a method for improving the generalizability of physics-informed neural networks (PINNs) for solving partial differential equations (PDEs). The method employs multi-task learning by training the network to solve multiple related PDEs simultaneously. An auxiliary PDE is constructed by varying the parameterization coefficient of the original PDE. The network is also trained with an uncertainty-weighting loss and adversarial samples from high-loss regions to focus learning on more challenging areas. The results show this multi-task learning approach reduces errors on unseen data compared to previous PINN methods.
Detailed Information
| Author: | ['Vichara Pools'] |
|---|---|
| Publication Year: | 2022 |
| Pages: | 4 |
| Language: | English |
| Format: | |
| Price: | FREE |
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