Summary of Joint Parameter and Parameterization Inference with Uncertainty Quantification Through Differentiable Programming, by Yongquan Qu et al.
Joint Parameter and Parameterization Inference with Uncertainty Quantification through Differentiable Programming
by Yongquan Qu, Mohamed Aziz Bhouri, Pierre Gentine
First submitted to arxiv on: 4 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Dynamical Systems (math.DS); Chaotic Dynamics (nlin.CD); Atmospheric and Oceanic Physics (physics.ao-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 The proposed framework combines machine learning (ML) and numerical simulations to model subgrid physical processes in complex systems, such as weather and climate prediction. The approach jointly estimates physical parameters and ML parameterizations while quantifying uncertainty. By incorporating online training and efficient Bayesian inference within a high-dimensional parameter space facilitated by differentiable programming, the framework synergistically combines ML with differential equations, enhancing the capabilities of hybrid physics-ML modeling. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a new way to combine machine learning (ML) with numerical simulations to model complex systems. The approach helps us understand and predict things like weather and climate patterns. It does this by using a special kind of programming that lets ML work well with equations from physics. This allows for more accurate predictions and better understanding of the world around us. |
Keywords
* Artificial intelligence * Bayesian inference * Machine learning
