Model input verification of large scale simulations

Abstract

Reliable simulations require accurate input data. Invalid values, missing data, and format inconsistencies can cause crashes or result distortions, compromising the findings. This paper presents a methodology for verifying the validity of input data in simulations, a process we term model input verification (MIV). We implement this approach in FabGuard, a toolset that uses established data schema and validation tools for simulation modelling. We formalize MIV patterns and create a verification pipeline for existing workflows. FabGuard’s applicability is demonstrated across three domains: conflict-driven migration, disaster evacuation, and disease spread models. We also explore Large Language Models (LLMs) for automating constraint generation. In a migration simulation case study, LLMs correctly inferred 22/23 developer-defined constraints, identified errors in existing constraints, and proposed new, valid ones. Our evaluation demonstrates that MIV is feasible on large datasets, with FabGuard processing 300 input files in 140 seconds and maintaining consistent performance across file sizes.