204 citations · 443 across the 11 of their papers we have counts for
11 papers
Sequential Designs for Filling Output Spaces
Shangkun Wang, Adam P. Generale, Surya R. Kalidindi +1
Space-filling designs are commonly used in computer experiments to fill the space of inputs so that the input-output relationship can be accurately estimated. However, in certain a…
Conglomerate Multi-Fidelity Gaussian Process Modeling, with Application to Heavy-Ion Collisions
Yi Ji, Henry Shaowu Yuchi, Derek Soeder +5
In an era where scientific experimentation is often costly, multi-fidelity emulation provides a powerful tool for predictive scientific computing. While there has been notable work…
Data Twinning
Akhil Vakayil, V. Roshan Joseph
In this work, we develop a method named Twinning, for partitioning a dataset into statistically similar twin sets. Twinning is based on SPlit, a recently proposed model-independent…
SPlit: An Optimal Method for Data Splitting
V. Roshan Joseph, Akhil Vakayil
In this article we propose an optimal method referred to as SPlit for splitting a dataset into training and testing sets. SPlit is based on the method of Support Points (SP), which…
Robust Experimental Designs for Model Calibration
Arvind Krishna, V. Roshan Joseph, Shan Ba +2
A computer model can be used for predicting an output only after specifying the values of some unknown physical constants known as calibration parameters. The unknown calibration p…
Adaptive design for Gaussian process regression under censoring
Jialei Chen, Simon Mak, V. Roshan Joseph +1
A key objective in engineering problems is to predict an unknown experimental surface over an input domain. In complex physical experiments, this may be hampered by response censor…
Deterministic Sampling of Expensive Posteriors Using Minimum Energy Designs
V. Roshan Joseph, Dianpeng Wang, Li Gu +2
Markov chain Monte Carlo (MCMC) methods require a large number of samples to approximate a posterior distribution, which can be costly when the likelihood or prior is expensive to…
Projected support points: a new method for high-dimensional data reduction
Simon Mak, V. Roshan Joseph
In an era where big and high-dimensional data is readily available, data scientists are inevitably faced with the challenge of reducing this data for expensive downstream computati…
An efficient surrogate model for emulation and physics extraction of large eddy simulations
Simon Mak, Chih-Li Sung, Xingjian Wang +5
In the quest for advanced propulsion and power-generation systems, high-fidelity simulations are too computationally expensive to survey the desired design space, and a new design…
Minimax and minimax projection designs using clustering
Simon Mak, V. Roshan Joseph
Minimax designs provide a uniform coverage of a design space by minimizing the maximum distance from any point in this space to its nearest des…
Composite Gaussian process models for emulating expensive functions
Shan Ba, V. Roshan Joseph
A new type of nonstationary Gaussian process model is developed for approximating computationally expensive functions. The new model is a composite of two Gaussian processes, where…