提出深度核化多输出高斯过程，有效建模多输出非线性依赖关系。
Deep Intrinsic Coregionalization Multi-Output Gaussian Process Surrogate with Active Learning
- 分层核化结构增强多输出间非线性依赖建模能力
- 在多个仿真任务中表现优于现有先进模型
- 结合主动学习，高效选择关键输入点
深度高斯过程（DGPs）是灵活的代理模型，可捕捉复杂函数。但将其扩展到多输出场景仍具挑战，主要在于如何高效建模输出间依赖关系。本文提出深度内在核化多输出高斯过程（deepICMGP），通过在各层引入分层核化结构，扩展了内在核化模型（ICM），能有效建模多输出间的非线性与结构性依赖，解决了传统多输出高斯过程的关键局限。我们在多个计算机仿真实验中对 deepICMGP 进行基准测试，结果表明其性能具有竞争力。此外，将主动学习策略融入 deepICMGP，优化序列设计任务，显著提升其为多输出系统高效选择信息量大的输入位置的能力。
原文摘要 · Abstract (English)
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.
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