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About Physics-Informed Deep-Learning For Scientific Computing PDF
The document discusses physics-informed neural networks (PINNs) for solving partial differential equations (PDEs) like the Poisson equation. PINNs encode the governing equations into the neural network training to approximate solutions. The author evaluates PINN performance on the Poisson equation with different source terms and configurations. While PINNs have potential to replace traditional solvers, accuracy and speed are still limiting factors. However, hybrid strategies combining PINNs with methods like multigrid could provide new linear solvers.
Detailed Information
| Author: | ['bluedancer'] |
|---|---|
| Publication Year: | 2021 |
| Pages: | 19 |
| Language: | English |
| Format: | |
| Price: | FREE |
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