6 papers
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…
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…
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…
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…