Abstract
Most computational analysts, as well as most governmental policy-makers and the public, view computational simulation accuracyAccuracy as a good agreementAgreement of simulation results with empirical measurements. However, decision-makers, such as business managers and safety regulators who rely on simulation for decision support, view computational simulation accuracy as much more than agreement of simulation results with experimental dataExperimental data. Decision-makers’ concept of accuracy is better captured by the term predictive capability of the simulation. Predictive capability meaning the use of a computational modelComputational model to foretell or forecastBenchmark forecast the response of a system to conditions without availableExperimental data experimental dataData, even for system responses that have never occurred in nature. This chapter makes this important distinction by discussing the crucial ingredients needed for predictive capabilityPredictive capability: code verificationCode verification, solution verification, model validation, model calibrationCalibration, and predictive uncertaintyUncertainty estimation. Each of these ingredients is required, whether the simulation results are used in the generation of new knowledgeKnowledge, or for decision support by business managers, government policy-makers, or safety regulators.
