Summary of A Probabilistic Framework For Learning Non-intrusive Corrections to Long-time Climate Simulations From Short-time Training Data, by Benedikt Barthel Sorensen et al.
A probabilistic framework for learning non-intrusive corrections to long-time climate simulations from short-time training data
by Benedikt Barthel Sorensen, Leonardo Zepeda-Núñez, Ignacio Lopez-Gomez, Zhong Yi Wan, Rob Carver, Fei Sha, Themistoklis Sapsis
First submitted to arxiv on: 2 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Dynamical Systems (math.DS); Atmospheric and Oceanic Physics (physics.ao-ph); Fluid Dynamics (physics.flu-dyn)
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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 tackles the long-standing challenge of simulating chaotic systems, such as turbulent flows. By leveraging neural networks, researchers propose a strategy to non-intrusively correct under-resolved simulations. The approach trains a post-processing correction operator on nudged high-fidelity reference simulations, allowing for learning the dynamics of the underlying system with limited training data. This enables accurate predictions over long time horizons, even when the training data is far from converged. The framework was tested on severely under-resolved simulations of quasi-geostrophic flow and successfully predicted anisotropic statistics over a 30-fold longer time horizon than the training data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to predict what will happen in a stormy weather forecast, but you can only see the clouds for a short time. This paper helps solve this problem by creating a way to correct computer simulations of chaotic systems, like turbulent flows, that are too simplified or short-term. The method uses special algorithms and neural networks to learn from limited data and make more accurate predictions about what might happen in the future. This could be useful for predicting extreme weather events due to climate change. In tests, this approach worked well on a type of fluid flow simulation. |
