5 papers · 1 filter
Supervised Learning of Functional Outcomes with Predictors at Different Scales: A Functional Gaussian Process Approach
R. Jacob Andros, Rajarshi Guhaniyogi, Devin Francom +1
The analysis of complex computer simulations, often involving functional data, presents unique statistical challenges. Conventional regression methods, such as function-on-function…
Bayesian Adaptive Polynomial Chaos Expansions
Kellin N. Rumsey, Devin Francom, Graham C. Gibson +2
Polynomial chaos expansions (PCE) are widely used for uncertainty quantification (UQ) tasks, particularly in the applied mathematics community. However, PCE has received comparativ…
A Review and Comparison of Different Sensitivity Analysis Techniques in Practice
Devin Francom, Abigael Nachtsheim
There exist many methods for sensitivity analysis readily available to the practitioner. While each seeks to help the modeler answer the same general question -- How do sources of…
Interpretable Deep Neural Network for Modeling Functional Surrogates
Yeseul Jeon, Rajarshi Guhaniyogi, Aaron Scheffler +2
Developing surrogates for computer models has become increasingly important for addressing complex problems in science and engineering. This article introduces an artificial intell…
Elastic Bayesian Model Calibration
Devin Francom, J. Derek Tucker, Gabriel Huerta +2
Functional data are ubiquitous in scientific modeling. For instance, quantities of interest are modeled as functions of time, space, energy, density, etc. Uncertainty quantificatio…