PAGP: A physics-assisted Gaussian process framework with active learning for forward and inverse problems of partial differential equations

TL;DR

This paper introduces PAGP, a physics-informed Gaussian process framework with active learning for solving forward and inverse PDE problems, integrating physical laws into GPR models for improved accuracy and efficiency.

Contribution

The paper develops a novel PAGP framework with continuous, discrete, and hybrid models that incorporate physical PDE information into Gaussian processes for solving forward and inverse problems.

Findings

Effective in solving PDE forward problems.

Accurate discovery of unknown PDE coefficients.

Hybrid model combines advantages of both approaches.

Abstract

In this work, a Gaussian process regression(GPR) model incorporated with given physical information in partial differential equations(PDEs) is developed: physics-assisted Gaussian processes(PAGP). The targets of this model can be divided into two types of problem: finding solutions or discovering unknown coefficients of given PDEs with initial and boundary conditions. We introduce three different models: continuous time, discrete time and hybrid models. The given physical information is integrated into Gaussian process model through our designed GP loss functions. Three types of loss function are provided in this paper based on two different approaches to train the standard GP model. The first part of the paper introduces the continuous time model which treats temporal domain the same as spatial domain. The unknown coefficients in given PDEs can be jointly learned with GP hyper-parameters by minimizing the designed loss function. In the discrete time models, we first choose a time discretization scheme to discretize the temporal domain. Then the PAGP model is applied at each time step together with the scheme to approximate PDE solutions at given test points of final time. To discover unknown coefficients in this setting, observations at two specific time are needed and a mixed mean square error function is constructed to obtain the optimal coefficients. In the last part, a novel hybrid model combining the continuous and discrete time models is presented. It merges the flexibility of continuous time model and the accuracy of the discrete time model. The performance of choosing different models with different GP loss functions is also discussed. The effectiveness of the proposed PAGP methods is illustrated in our numerical section.