Inverse Problem for Dynamic Computer Simulators via Multiple Scalar-valued Contour Estimation

TL;DR

This paper introduces a sequential scalar-valued contour estimation method for solving inverse problems in dynamic computer simulators, using discretization and spline smoothing to improve accuracy.

Contribution

It proposes a novel approach that discretizes time-series outputs and iteratively solves scalar inverse problems, enhancing inverse problem solutions for dynamic simulators.

Findings

Effective in test-function based simulators

Successful application to rainfall-runoff model

Improves inverse problem accuracy

Abstract

In this paper we consider a dynamic computer simulator that produces a time-series response $y_t(x)$ over $L$ time points, for every given input parameter $x$. We propose a method for solving inverse problems, which refer to the finding of a set of inputs that generates a pre-specified simulator output. Inspired by the sequential approach of contour estimation via expected improvement criterion developed by Ranjan et al. (2008, DOI: 10.1198/004017008000000541), our proposed method discretizes the target response series on $k \; (\ll L)$ time points, and then iteratively solves $k$ scalar-valued inverse problems with respect to the discretized targets. We also propose to use spline smoothing of the target response series to identify the optimal number of knots, $k$, and the actual location of the knots for discretization. The performance of the proposed methods is compared for several test-function based computer simulators and the motivating real application that uses a rainfall-runoff measurement model named Matlab-Simulink model.