Summary of Assessment Of Uncertainty Quantification in Universal Differential Equations, by Nina Schmid et al.
Assessment of Uncertainty Quantification in Universal Differential Equations
by Nina Schmid, David Fernandes del Pozo, Willem Waegeman, Jan Hasenauer
First submitted to arxiv on: 13 Jun 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Quantitative Methods (q-bio.QM)
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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 Scientific Machine Learning approach integrates physical knowledge and mechanistic models with data-driven techniques to uncover governing equations of complex processes. The Universal Differential Equations (UDEs) method combines prior knowledge in the form of mechanistic formulations with neural networks, estimating parameters jointly using empirical data. While robust models rely on rigorous uncertainty quantification, this work formalizes UQ for UDEs and investigates frequentist and Bayesian methods. Synthetic examples demonstrate the efficacy of ensembles, variational inference, and Markov chain Monte Carlo sampling as epistemic UQ methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Scientists are using a new way to understand complex things that happen in nature or machines. They’re combining what we already know about how something works with special computer programs that learn from data. This helps them figure out the rules that govern those complex processes. The key is to be really good at guessing how certain some of their findings are, which is called uncertainty quantification. In this research, they developed a way to do that for a specific type of model called Universal Differential Equations. They tested it on some fake examples and found that different methods worked better or worse depending on the situation. |
Keywords
» Artificial intelligence » Inference » Machine learning
