Summary of Multi-fidelity Machine Learning For Uncertainty Quantification and Optimization, by Ruda Zhang and Negin Alemazkoor
Multi-fidelity Machine Learning for Uncertainty Quantification and Optimization
by Ruda Zhang, Negin Alemazkoor
First submitted to arxiv on: 30 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Optimization and Control (math.OC); Machine Learning (stat.ML)
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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 emerging field of machine learning-based multi-fidelity methods integrates high- and low-fidelity models to balance computational cost and predictive accuracy. This perspective paper provides an overview of the current state of the art, identifying critical gaps in the literature and outlining key research opportunities. The authors focus on uncertainty quantification using multi-fidelity graph neural networks and polynomial chaos expansion, as well as optimization through Bayesian methods with multi-fidelity priors. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning is being used to improve our understanding of physical systems by combining different models that are more or less accurate. This helps balance the need for precise predictions with the need to use computers efficiently. The paper looks at how machine learning can be used to quantify uncertainty and make better decisions about complex systems. It also explores how this technology can be applied to solve real-world problems. |
Keywords
* Artificial intelligence * Machine learning * Optimization
