Summary of Multi-fidelity Gaussian Process Surrogate Modeling For Regression Problems in Physics, by Kislaya Ravi et al.
Multi-fidelity Gaussian process surrogate modeling for regression problems in physics
by Kislaya Ravi, Vladyslav Fediukov, Felix Dietrich, Tobias Neckel, Fabian Buse, Michael Bergmann, Hans-Joachim Bungartz
First submitted to arxiv on: 18 Apr 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Data Analysis, Statistics and Probability (physics.data-an)
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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 This paper explores the challenges of surrogate modeling when faced with limited data availability due to computationally expensive simulations. To address this issue, multi-fidelity methods chain models in a hierarchy, increasing fidelity and decreasing error at the cost of increased computation. The authors compare various multi-fidelity methods for constructing Gaussian process surrogates for regression tasks, extending existing non-linear autoregressive methods to handle more than two levels of fidelity. Additionally, they propose enhancements to an existing method by introducing structured kernels with delay terms. The paper demonstrates the performance of these methods across different academic and real-world scenarios, showing that multi-fidelity methods generally have a smaller prediction error at similar computational costs compared to single-fidelity methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research explores ways to create more accurate models using limited data. When it takes too long or is too expensive to run computer simulations, we can use lower-cost models as a “stand-in” to make predictions. The authors looked at different techniques for creating these “surrogate” models and found that by combining multiple models with increasing levels of detail, they could get more accurate results with less computational effort. They tested their methods on various problems and showed that they often do better than using just one model. |
Keywords
» Artificial intelligence » Autoregressive » Regression
