Summary of Neural Laplace For Learning Stochastic Differential Equations, by Adrien Carrel
Neural Laplace for learning Stochastic Differential Equations
by Adrien Carrel
First submitted to arxiv on: 7 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Probability (math.PR)
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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 Neural Laplace is a unified framework for learning various types of differential equations (DEs), outperforming other approaches in learning ordinary differential equations (ODEs). However, many systems cannot be modeled using ODEs. Neural Laplace has the potential to learn stochastic differential equations (SDEs) from both theoretical and practical perspectives. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This framework can help model spatiotemporal dynamics with randomness, which is useful in various fields like physics, biology, and economics. By learning diverse classes of SDEs, Neural Laplace can be applied to a wide range of problems that involve uncertainty and complexity. |
Keywords
» Artificial intelligence » Spatiotemporal
