Summary of Parametric Taylor Series Based Latent Dynamics Identification Neural Networks, by Xinlei Lin and Dunhui Xiao
Parametric Taylor series based latent dynamics identification neural networks
by Xinlei Lin, Dunhui Xiao
First submitted to arxiv on: 5 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Neural and Evolutionary Computing (cs.NE); Dynamical Systems (math.DS)
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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 paper proposes a novel approach to solving parameterised partial differential equations (P-PDEs) using reduced-order models (ROMs). By combining latent space identification techniques with deep learning algorithms, such as autoencoders, the authors demonstrate the potential of this method in describing dynamical systems in lower-dimensional latent spaces. Specifically, they showcase the effectiveness of LaSDI, gLaSDI, and GPLaSDI methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Solving partial differential equations is important but takes a lot of computing power. To make it more efficient, scientists have developed reduced-order models that use fewer calculations to get similar results. Recently, new ways were discovered to combine these models with deep learning, which is like teaching computers how to learn. This helps describe complex systems in simpler terms. The authors show that using techniques like LaSDI, gLaSDI, and GPLaSDI can be very useful. |
Keywords
» Artificial intelligence » Deep learning » Latent space
