Gaussian process regression and conditional polynomial chaos for parameter estimation

We present a new approach for constructing a data-driven surrogate model and using it for parameter estimation in partial differential equation (PDE) models. We first use parameter observations and Gaussian process regression to condition the Karhunen–Loéve (KL) expansion of the unknown space-dependent parameters and then build the conditional generalized polynomial chaos (gPC) surrogate model of the PDE states. Next, we estimate the unknown parameters by computing coefficients in the KL expansion by minimizing the square difference between the gPC predictions and measurements of the states. Our approach addresses two major challenges in the parameter estimation. First, it reduces dimensionality of the parameter space and replaces expensive, direct solutions of PDEs with the conditional gPC surrogates. Second, the estimated parameter field exactly matches the parameter measurements. In addition, we show that the conditional gPC surrogate can be used to estimate the state variance, that, in turn, can be used to guide data acquisition. We demonstrate that our approach improves the accuracy of parameter estimation with application to one- and two-dimensional Darcy equations that have (unknown) space-dependent hydraulic conductivity. We also discuss the effect of hydraulic conductivity and head locations on the accuracy of the hydraulic conductivity estimations. © 2020 Elsevier Inc.