Summary of A Primer on Variational Inference For Physics-informed Deep Generative Modelling, by Alex Glyn-davies et al.
A Primer on Variational Inference for Physics-Informed Deep Generative Modelling
by Alex Glyn-Davies, Arnaud Vadeboncoeur, O. Deniz Akyildiz, Ieva Kazlauskaite, Mark Girolami
First submitted to arxiv on: 10 Sep 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Computational Physics (physics.comp-ph)
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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 Variational inference (VI) is a computationally efficient methodology for approximate Bayesian inference, offering a balance between accuracy and tractability. It excels in generative modelling and inversion tasks due to its built-in Bayesian regularisation and flexibility, essential qualities for physics-related problems. The paper provides an accessible introduction to VI for forward and inverse problems, covering standard derivations of the VI framework and its implementation through deep learning. Recent literature is reviewed and unified, showcasing VI’s creative flexibility. This methodology is particularly relevant for solving physics-based problems that require uncertainty quantification. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about a way to do complex calculations in physics called Variational Inference (VI). It’s an efficient method for figuring out what things might be like if we don’t know everything. VI is good at solving problems that involve generating new data or reversing what happened in the past. The authors explain how VI works and show examples of how it can be used to solve real-world physics problems. They also review what others have done with VI and how it can be applied. |
Keywords
» Artificial intelligence » Bayesian inference » Deep learning » Inference
