Summary of Learning Latent Space Dynamics with Model-form Uncertainties: a Stochastic Reduced-order Modeling Approach, by Jin Yi Yong and Rudy Geelen and Johann Guilleminot
Learning Latent Space Dynamics with Model-Form Uncertainties: A Stochastic Reduced-Order Modeling Approach
by Jin Yi Yong, Rudy Geelen, Johann Guilleminot
First submitted to arxiv on: 30 Aug 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 probabilistic approach uses operator inference techniques to represent and quantify model-form uncertainties in reduced-order modeling of complex systems. This method, building upon previous works, expands the approximation space by randomizing the projection matrix, combining Riemannian projection and retraction operators on the Stiefel manifold with an information-theoretic formulation. The efficacy is demonstrated on canonical fluid mechanics problems, identifying and quantifying the impact of model-form uncertainties on inferred operators. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand complex systems better by figuring out how to include uncertainty in our models. It’s like trying to draw a picture of something, but instead of drawing it perfectly, we add some randomness to make sure our picture isn’t too perfect. This makes our model more realistic and accurate. The authors tested this idea on problems related to fluid flow and showed that it works well. |
Keywords
» Artificial intelligence » Inference
