4 citations · 9 across the 11 of their papers we have counts for
8 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…
Discovering Active Subspaces for High-Dimensional Computer Models
Kellin N. Rumsey, Devin Francom, Scott Vander Wiel
Dimension reduction techniques have long been an important topic in statistics, and active subspaces (AS) have received much attention this past decade in the computer experiments…
Generalized Bayesian MARS: Tools for Emulating Stochastic Computer Models
Kellin Rumsey, Devin Francom, Andy Shen
The multivariate adaptive regression spline (MARS) approach of Friedman (1991) and its Bayesian counterpart (Francom et al. 2018) are effective approaches for the emulation of comp…
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…
Bayesian Projection Pursuit Regression
Gavin Collins, Devin Francom, Kellin Rumsey
In projection pursuit regression (PPR), an unknown response function is approximated by the sum of M "ridge functions," which are flexible functions of one-dimensional projections…