Subtrace-Conditional Validation of Simulation Models and Digital Twins

2026-07-19

AI Summary

This study addresses the challenge of effectively validating input model specifications in digital twin simulations, where conventional approaches—relying solely on marginal output distributions—often fail to detect misspecified joint input models. To overcome this limitation, the authors propose a novel statistical validation framework based on sub-trajectory conditioning. By repeatedly restarting simulations from observed system states while conditioning on subsets of random inputs, the method constructs conditional output distributions that enable goodness-of-fit testing of the full joint input model. This approach innovatively transcends the constraints of marginal validation and is complemented by diagnostic tools to pinpoint specific input sources responsible for detected discrepancies. Empirical evaluations on M/M/1 and tandem queueing systems demonstrate the framework’s heightened sensitivity and effectiveness, successfully identifying input model misspecifications that traditional methods overlook.

Abstract

Validating simulation models against historical output data is essential for their successful deployment in digital-twin environments. We propose a statistical validation framework in which a simulation model is repeatedly initialized from observed system states, and conditional output distributions are obtained by fixing the random primitives from a subset of stochastic input models to their observed realizations while simulating the remaining primitives. These conditional output distributions are then used in goodness-of-fit tests to validate the simulation model with respect to combinations of input models. We also develop diagnostic tools to identify the input models that most contribute to any observed misalignment between a simulation model's outputs and reality. Numerical experiments on an M/M/1 queueing system and a digital-twin-enabled simulation of a tandem queueing system demonstrate that the proposed framework can detect misspecifications in input models that may be missed by existing approaches that validate only the marginal output distribution.

Problem

• simulation validation
• digital twins
• input model misspecification
• conditional output distribution
• goodness-of-fit

Innovation

• conditional validation
• digital twin
• simulation model validation
• stochastic input models
• goodness-of-fit test