Probabilistic Validation Of Complex Engineering Simulations With Sparse Data

The challenges of validating expensive simulations with very sparse experimental data are addressed in this article. The effects of uncertainties in simulations and test data and their impact on validation are highlighted using several examples. Bayesian methods for calibration and tool validation are presented as the primary techniques for performing probabilistic validation. The applicability of the methods to complex engineering problems is discussed. The effect of prior assumptions on calibration and validation are explained through simple illustrative examples. Guidelines for planning simulations and experiments for validation are also provided.