Monday, February 10 2020
11:00am - 12:00pm
Computational Mathematics Colloquium
Computational Mathematics Colloquium: From HDMR to FAST-HDMR: Surrogate Modeling for Uncertainty Quantification

Surrogate modeling is a popular and practical method to meet the needs of a large number of queries of computationally demanding models in the analysis of uncertainty, sensitivity and system reliability. We first explore various methods that can improve the accuracy of a particular class of surrogate models, the high dimensional model representation (HDMR), and their performances in uncertainty quantification and variance-based global sensitivity analysis. The efficiency of our proposed methods is demonstrated by a few analytical examples that are commonly studied for uncertainty and sensitivity analysis algorithms. HDMR techniques are also applied to an operational wildland fire model that is widely employed in fire prevention and safety control, and a chemical kinetics H2/air combustion model predicting the ignition delay time, which plays an important role in studying fuel and combustion system reliability and safety. We then show how the traditional Fourier Amplitude Sensitivity Testing (FAST), heavily used for variance-based global sensitivity analysis, can be treated in the framework of HDMR. The resulting surrogate model, named FAST-HDMR, is shown to be computationally more efficient then the original FAST. Various improvements that further enhance the accuracy of FAST-HDMR are discussed and illustrated by examples.
Speaker:Yaning Liu
Affiliation:CU Denver Department of Mathematical and Statistical Sciences
Location:SCB 4017


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