Summary of Multifidelity Surrogate Models: a New Data Fusion Perspective, by Daniel N Wilke
Multifidelity Surrogate Models: A New Data Fusion Perspective
by Daniel N Wilke
First submitted to arxiv on: 21 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Numerical Analysis (math.NA)
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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 multifidelity surrogate modeling approach combines data from different sources, leveraging both high- and low-fidelity models to optimize decision-making. By strategically using low-fidelity models for rapid evaluations and high-fidelity models for detailed refinement, this method improves upon traditional single-fidelity models, which either oversimplify or are computationally intensive. The authors demonstrate the potential of multifidelity surrogate modeling in various domains, including design optimization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper combines different types of data to make better decisions. It uses simple models that can be computed quickly and more detailed models that require more time and resources. By combining these two types of models, it’s possible to make more accurate predictions and optimize designs in different areas, such as engineering or finance. |
Keywords
» Artificial intelligence » Optimization
