论文标题
对受限的高斯流程回归的调查：方法和实施挑战
A Survey of Constrained Gaussian Process Regression: Approaches and Implementation Challenges
论文作者
论文摘要
高斯流程回归是昂贵数据源的替代建模的流行贝叶斯框架。作为科学机器学习的更广泛努力的一部分，许多最近的作品已在高斯流程回归中纳入了物理约束或其他先验信息，以补充有限的数据并正常模型的行为。我们提供了几类高斯过程约束的概述和调查，包括阳性或界限约束，单调性和凸约限制，线性PDE提供的微分方程约束以及边界条件约束。我们比较了每种方法背后的策略以及实施差异，并讨论了约束所引入的计算挑战。
Gaussian process regression is a popular Bayesian framework for surrogate modeling of expensive data sources. As part of a broader effort in scientific machine learning, many recent works have incorporated physical constraints or other a priori information within Gaussian process regression to supplement limited data and regularize the behavior of the model. We provide an overview and survey of several classes of Gaussian process constraints, including positivity or bound constraints, monotonicity and convexity constraints, differential equation constraints provided by linear PDEs, and boundary condition constraints. We compare the strategies behind each approach as well as the differences in implementation, concluding with a discussion of the computational challenges introduced by constraints.
