Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.16434 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912549254987776 |
|---|---|
| author | Chang, Chun-Yi Sung, Chih-Li |
| author_facet | Chang, Chun-Yi Sung, Chih-Li |
| contents | Deep Gaussian Processes (DGPs) are powerful surrogate models known for their flexibility and ability to capture complex functions. However, extending them to multi-output settings remains challenging due to the need for efficient dependency modeling. We propose the Deep Intrinsic Coregionalization Multi-Output Gaussian Process (deepICMGP) surrogate for computer simulation experiments involving multiple outputs, which extends the Intrinsic Coregionalization Model (ICM) by introducing hierarchical coregionalization structures across layers. This enables deepICMGP to effectively model nonlinear and structured dependencies between multiple outputs, addressing key limitations of traditional multi-output GPs. We benchmark deepICMGP against state-of-the-art models, demonstrating its competitive performance. Furthermore, we incorporate active learning strategies into deepICMGP to optimize sequential design tasks, enhancing its ability to efficiently select informative input locations for multi-output systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_16434 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Deep Intrinsic Coregionalization Multi-Output Gaussian Process Surrogate with Active Learning Chang, Chun-Yi Sung, Chih-Li Machine Learning Deep Gaussian Processes (DGPs) are powerful surrogate models known for their flexibility and ability to capture complex functions. However, extending them to multi-output settings remains challenging due to the need for efficient dependency modeling. We propose the Deep Intrinsic Coregionalization Multi-Output Gaussian Process (deepICMGP) surrogate for computer simulation experiments involving multiple outputs, which extends the Intrinsic Coregionalization Model (ICM) by introducing hierarchical coregionalization structures across layers. This enables deepICMGP to effectively model nonlinear and structured dependencies between multiple outputs, addressing key limitations of traditional multi-output GPs. We benchmark deepICMGP against state-of-the-art models, demonstrating its competitive performance. Furthermore, we incorporate active learning strategies into deepICMGP to optimize sequential design tasks, enhancing its ability to efficiently select informative input locations for multi-output systems. |
| title | Deep Intrinsic Coregionalization Multi-Output Gaussian Process Surrogate with Active Learning |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2508.16434 |