UC Berkeley

Model validation is the process of evaluating how well a computational model represents reality. That is to say, does the model make predictions that adequately agree with the experimental evidence? Both model validation and uncertainty quantification have gained tremendous attention from researchers in engineering, physics, chemistry, and biology. Uncertainty quantification methods have been successfully applied to assessing model predictions of unmeasured quantities of interest and assisting in the development of computationally efficient, yet predictive, reduced-order models. In both cases, experimental data are incorporated into the analysis to refine the uncertainty estimate. However, with the amount of experimental data published and being generated through ongoing scientific endeavors, it is crucial to organize and integrate experimental data with the uncertainty quantification methods.

In this work, I develop tools for uncertainty quantification and construct a validation workflow that seamlessly integrates uncertainty quantification tools with an online database of chemical kinetics validation data. The first part of this dissertation discusses the need for structured experimental data, emphasizing its value towards model validation, and explore how online databases provide structure to data. An optimization-based framework for uncertainty quantification, Bound-to-Bound Data Collaboration, is employed throughout the dissertation to verify the compatibility of models with data. A novel strategy for surrogate modeling using Bound-to-Bound Data Collaboration is developed to guide the fitting procedure towards regions of the parameter space where the model predicts the data accurately. This technique is demonstrated in two simple examples and a solid-fuel combustion example. In the second part of this dissertation, three complex physics-based models are investigated, specifically H2/O2 combustion, a solid-fuel char oxidation model, and a semi-empirical quantum chemistry model. The efficacy of the validation workflow for developing predictive models, and the scientific insights uncovered from the analysis, is discussed.