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About Physics-Informed Neural Networks
This document describes a new framework called physics-informed neural networks that uses neural networks to solve forward and inverse problems involving nonlinear partial differential equations. The framework trains neural networks to respect physical laws described by PDEs. This allows the neural networks to be data-efficient and generalize well even with limited data. The framework can be used for data-driven solutions of PDEs and data-driven discovery of PDEs from limited observations. It is demonstrated on problems in fluids, quantum mechanics, reaction-diffusion systems, and shallow water waves.
Detailed Information
| Author: | ['yw42ysdxqq'] |
|---|---|
| Publication Year: | 2024 |
| Pages: | 22 |
| Language: | English |
| Format: | |
| Price: | FREE |
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