Summary of Multi-fidelity Methods For Optimization: a Survey, by Ke Li and Fan Li
Multi-Fidelity Methods for Optimization: A Survey
by Ke Li, Fan Li
First submitted to arxiv on: 15 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Neural and Evolutionary Computing (cs.NE)
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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 A novel text mining framework based on a pre-trained language model drives a systematic exploration of multi-fidelity optimization (MFO), a cost-effective strategy balancing high-fidelity accuracy with computational efficiency. The survey delves into MFO’s foundational principles, methodologies, and applications across machine learning, engineering design optimization, and scientific discovery. Key components include multi-fidelity surrogate models, fidelity management strategies, and optimization techniques. As MFO tackles complex challenges, scalability, lower-fidelity composition, and human-in-the-loop approaches become critical issues. The survey aims to catalyze further research and foster collaborations in MFO, setting the stage for future innovations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Multi-fidelity optimization (MFO) is a way to make computer simulations faster and more accurate. Imagine trying to find the best settings for a recipe by testing different ingredients, cooking times, and temperatures. You don’t want to waste time making many of these combinations, so you use a shortcut: a simpler model that’s close enough to help you find the perfect mix. This survey looks at how MFO works, its advantages, and where it can be used (like in machine learning or engineering design). It also talks about some challenges and ways to improve this approach. |
Keywords
* Artificial intelligence * Language model * Machine learning * Optimization
