A Survey of Constrained Gaussian Process Regression: Approaches and Implementation Challenges

TL;DR

This paper surveys various constrained Gaussian process regression methods, highlighting their strategies, implementation challenges, and how they incorporate physical or prior information to improve surrogate modeling.

Contribution

It provides a comprehensive overview of different constraint types in Gaussian process regression and discusses the computational challenges involved in their implementation.

Findings

Different constraint strategies have unique implementation approaches.

Constraints improve model regularization and data efficiency.

Computational challenges include increased complexity and scalability issues.

Abstract

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.