Summary of Densely Multiplied Physics Informed Neural Networks, by Feilong Jiang et al.
Densely Multiplied Physics Informed Neural Networks
by Feilong Jiang, Xiaonan Hou, Min Xia
First submitted to arxiv on: 6 Feb 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 paper proposes a novel neural network architecture called densely multiply physics-informed neural networks (DM-PINNs) to improve the precision of physics-informed neural networks (PINNs) for solving nonlinear partial differential equations (PDEs). Unlike traditional optimization-based approaches, DM-PINNs enhance the performance by multiplying the output of hidden layers with previous layer outputs. The proposed architecture is evaluated on four benchmark examples, showcasing superior accuracy and efficiency compared to existing PINN structures. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way for neural networks called densely multiply PINNs (DM-PINNs) to solve complex math problems like partial differential equations. It’s better than other methods because it makes the network more accurate without adding extra things to learn. The researchers tested DM-PINNs on four examples and showed that they work really well. |
Keywords
* Artificial intelligence * Neural network * Optimization * Precision
