Constrained Gaussian Random Fields with Continuous Linear Boundary Restrictions for Physics-informed Modeling of States

TL;DR

This paper introduces a new framework for constructing Gaussian random fields that exactly satisfy linear boundary constraints, enhancing modeling of physical states with known boundary conditions.

Contribution

The work develops a general method to create boundary-constrained Gaussian random fields from unconstrained ones, enabling more accurate physics-informed modeling.

Findings

Improved predictive performance in boundary-constrained state estimation

More realistic uncertainty quantification in physical models

Enhanced modeling of smooth states with boundary conditions

Abstract

Boundary constraints in physical, environmental and engineering models restrict smooth states such as temperature to follow known physical laws at the edges of their spatio-temporal domain. Examples include fixed-state or fixed-derivative (insulated) boundary conditions, and constraints that relate the state and the derivatives, such as in models of heat transfer. Despite their flexibility as prior models over system states, Gaussian random fields do not in general enable exact enforcement of such constraints. This work develops a new general framework for constructing linearly boundary-constrained Gaussian random fields from unconstrained Gaussian random fields over multi-dimensional, convex domains. This new class of models provides flexible priors for modeling smooth states with known physical mechanisms acting at the domain boundaries. Simulation studies illustrate how such physics-informed probability models yield improved predictive performance and more realistic uncertainty quantification in applications including probabilistic numerics, data-driven discovery of dynamical systems, and boundary-constrained state estimation, as compared to unconstrained alternatives.