Optimization-based Calibration of Simulation Input Models

TL;DR

This paper introduces an optimization framework for calibrating simulation input models using only output data, providing statistically valid bounds and analyzing their guarantees.

Contribution

It presents a novel inverse problem approach for nonparametric input calibration based solely on output data, with an iterative solution method and statistical guarantees.

Findings

Successfully bounds input models and performance measures

Demonstrates statistical validity of the bounds

Shows effectiveness through numerical experiments

Abstract

Studies on simulation input uncertainty often built on the availability of input data. In this paper, we investigate an inverse problem where, given only the availability of output data, we nonparametrically calibrate the input models and other related performance measures of interest. We propose an optimization-based framework to compute statistically valid bounds on input quantities. The framework utilizes constraints that connect the statistical information of the real-world outputs with the input-output relation via a simulable map. We analyze the statistical guarantees of this approach from the view of data-driven robust optimization, and show how the guarantees relate to the function complexity of the constraints arising in our framework. We investigate an iterative procedure based on a stochastic quadratic penalty method to approximately solve the resulting optimization. We conduct numerical experiments to demonstrate our performance in bounding the input models and related quantities.