1 citations · 1 across the 2 of their papers we have counts for
10 papers
Sampling-Based Batch Sequential Design by Stein Variational Gradient Descent
Penghui Fu, Xiaoxian Ding, Chunlin Ji +2
Many real-world experimental design problems require a batch of experimental runs across stages, in which multiple points are selected and evaluated at each stage. However, most wo…
Parameter Inference based on Gaussian Processes Informed by Nonlinear Partial Differential Equations
Zhaohui Li, Shihao Yang, Jeff Wu
Partial differential equations (PDEs) are widely used for the description of physical and engineering phenomena. Some key parameters involved in PDEs, which represent certain physi…
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…
A hierarchical expected improvement method for Bayesian optimization
Zhehui Chen, Simon Mak, C. F. Jeff Wu
The Expected Improvement (EI) method, proposed by Jones et al. (1998), is a widely-used Bayesian optimization method, which makes use of a fitted Gaussian process model for efficie…
Analysis-of-marginal-Tail-Means (ATM): a robust method for discrete black-box optimization
Simon Mak, C. F. Jeff Wu
We present a new method, called Analysis-of-marginal-Tail-Means (ATM), for effective robust optimization of discrete black-box problems. ATM has important applications to many real…
On Prediction Properties of Kriging: Uniform Error Bounds and Robustness
Wenjia Wang, Rui Tuo, C. F. Jeff Wu
Kriging based on Gaussian random fields is widely used in reconstructing unknown functions. The kriging method has pointwise predictive distributions which are computationally simp…