Date of Award
Summer 2018
Document Type
Open Access Dissertation
Department
Computer Science and Engineering
First Advisor
Gabriel Terejanu
Abstract
Computational models play an important role in scientific discovery and engineering design. However, developing computational models is challenging, since the process always follows a path contaminated with errors and uncertainties. The uncertainties and errors inherent in computational models are the result of many factors, including experimental uncertainties, model structure inadequacies, uncertainties in model parameters and initial conditions, as well as errors due to numerical discretizations. To realize the full potential in applications it is critical to systematically and economically reduce the uncertainties inherent in all computational models.
Rights
© 2018, Xiao Lin
Recommended Citation
Lin, X.(2018). Inference Framework for Model Update and Development. (Doctoral dissertation). Retrieved from https://scholarcommons.sc.edu/etd/4921