Design of Experiments for Emulations: A Selective Review from a Modeling Perspective
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866909721442648064 |
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| author | Deng, Xinwei Kang, Lulu Lin, C. Devon |
| author_facet | Deng, Xinwei Kang, Lulu Lin, C. Devon |
| contents | Space-filling designs are crucial for efficient computer experiments, enabling accurate surrogate modeling and uncertainty quantification in many scientific and engineering applications, such as digital twin systems and cyber-physical systems. In this work, we will provide a comprehensive review on key design methodologies, including Maximin/miniMax designs, Latin hypercubes, and projection-based designs. Moreover, we will connect the space-filling design criteria like the fill distance to Gaussian process performance. Numerical studies are conducted to investigate the practical trade-offs among various design types, with the discussion on emerging challenges in high-dimensional and constrained settings. The paper concludes with future directions in adaptive sampling and machine learning integration, providing guidance for improving computational experiments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_09596 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Design of Experiments for Emulations: A Selective Review from a Modeling Perspective Deng, Xinwei Kang, Lulu Lin, C. Devon Methodology G.3 Space-filling designs are crucial for efficient computer experiments, enabling accurate surrogate modeling and uncertainty quantification in many scientific and engineering applications, such as digital twin systems and cyber-physical systems. In this work, we will provide a comprehensive review on key design methodologies, including Maximin/miniMax designs, Latin hypercubes, and projection-based designs. Moreover, we will connect the space-filling design criteria like the fill distance to Gaussian process performance. Numerical studies are conducted to investigate the practical trade-offs among various design types, with the discussion on emerging challenges in high-dimensional and constrained settings. The paper concludes with future directions in adaptive sampling and machine learning integration, providing guidance for improving computational experiments. |
| title | Design of Experiments for Emulations: A Selective Review from a Modeling Perspective |
| topic | Methodology G.3 |
| url | https://arxiv.org/abs/2505.09596 |