4 citations · 7 across the 9 of their papers we have counts for
10 papers
A Review of Bayesian Surrogate Modeling for High-Dimensional Computer Simulations
Gavin Collins, Gabriel Huerta, J. Derek Tucker +1
Bayesian surrogate models play a central role in the analysis of computationally expensive physics-based simulators, particularly when uncertainty quantification (UQ) is a primary…
Ribbon: Scalable Approximation and Robust Uncertainty Quantification
Graham Gibson, John Tipton, Kellin Rumsey +1
Reliably quantifying predictive uncertainty is difficult for complex, high-dimensional, or misspecified models. Both fully Bayesian and bootstrap resampling methods provide princip…
All Emulators are Wrong, Many are Useful, and Some are More Useful Than Others: A Reproducible Comparison of Computer Model Surrogates
Kellin N. Rumsey, Graham C. Gibson, Devin Francom +1
Accurate and efficient surrogate modeling is essential for modern computational science, and there are a staggering number of emulation methods to choose from. With new methods bei…
A Partitioned Sparse Variational Gaussian Process for Fast, Distributed Spatial Modeling
Michael Grosskopf, Kellin Rumsey, Ayan Biswas +1
The next generation of Department of Energy supercomputers will be capable of exascale computation. For these machines, far more computation will be possible than that which can be…
Enhancing Approximate Modular Bayesian Inference by Emulating the Conditional Posterior
Grant Hutchings, Kellin Rumsey, Derek Bingham +1
In modular Bayesian analyses, complex models are composed of distinct modules, each representing different aspects of the data or prior information. In this context, fully Bayesian…
Fast Emulation, Modular Calibration, and Active Learning for Simulators with Functional Response
Grant Hutchings, Derek Bingham, Kellin Rumsey +1
Scalable surrogate models enable efficient emulation of computer models (or simulators), particularly when dealing with large ensembles of runs. While Gaussian process (GP) models…
Co-Active Subspace Methods for the Joint Analysis of Adjacent Computer Models
Kellin N. Rumsey, Zachary K. Hardy, Cory Ahrens +1
Active subspace (AS) methods are a valuable tool for understanding the relationship between the inputs and outputs of a Physics simulation. In this paper, an elegant generalization…
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…
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…