204 citations · 443 across the 11 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…