Validation and Verification Techniques for Ensuring Accuracy in Modeling and Simulation Systems

Abstract

Modelling and simulation have become indispensable tools across various scientific and engineering domains, supporting design optimization, risk evaluation, and policy decision-making. However, the increasing reliance on simulation outcomes necessitates rigorous verification and validation (V&V) to ensure model credibility, especially in high-impact fields such as aerospace, biomedicine, climate modelling, and defence. This study aims to review, compare, and synthesize contemporary V&V techniques and evaluate their practical effectiveness through domain-specific applications. Using a structured comparative methodology, we analyze core V&V practices, including code review, formal verification, experimental validation, predictive validation, and uncertainty quantification. A usage intensity scale (1–10) was introduced to quantify technique adoption, revealing that experimental data comparison is the most frequently applied method (9/10), followed by code review and uncertainty quantification (both 8/10). Case studies show that aerospace and defence domains report the highest V&V impact, with confidence gain and decision support rated at 9/10. At the same time, biomedical and climate modelling demonstrate slightly lower scores due to biological variability and system complexity. The novelty of this study lies in presenting an integrated cross-domain V&V framework and in highlighting the growing need for adaptive, hybrid validation methods tailored for emerging AI-driven and data-intensive models. Despite evident benefits, enhanced confidence, reduced costs, and improved decision-making challenges remain, particularly in novel simulation environments with limited validation data. In conclusion, this article reinforces the critical role of V&V in ensuring simulation reliability and calls for innovation in V&V strategies to match the evolving complexity of modern simulation systems.