Summary of Binary Structured Physics-informed Neural Networks For Solving Equations with Rapidly Changing Solutions, by Yanzhi Liu and Ruifan Wu and Ying Jiang
Binary structured physics-informed neural networks for solving equations with rapidly changing solutions
by Yanzhi Liu, Ruifan Wu, Ying Jiang
First submitted to arxiv on: 23 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research proposes a novel approach to solving partial differential equations (PDEs) using binary structured physics-informed neural networks (BsPINNs). By incorporating physical information into feedforward neural networks, BsPINNs can learn complex relationships between variables without requiring labeled data. The authors demonstrate the effectiveness of BsPINNs in capturing local features and improving solution accuracy compared to traditional physics-informed neural networks (PINNs). In a series of numerical experiments solving various PDEs, including Burgers equation, Euler equation, Helmholtz equation, and high-dimension Poisson equation, BsPINNs exhibit superior convergence speed and heightened accuracy. The findings highlight the potential of BsPINNs to resolve issues caused by increased hidden layers in PINNs and prevent declines in accuracy due to non-smoothness of PDEs solutions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Physics-informed neural networks (PINNs) are a type of deep learning that helps solve partial differential equations (PDEs). PINNs work well, but sometimes they get stuck or take too long to find the correct answer. To fix this, scientists created a new kind of PINN called BsPINNs. BsPINNs use special connections between neurons that make them better at finding local patterns in data. This helps them solve PDEs more accurately and quickly. The researchers tested BsPINNs on several different PDEs and found that they worked much better than traditional PINNs. |
Keywords
* Artificial intelligence * Deep learning
