Multi-resolution functional ANOVA for large-scale, many-input computer experiments

Chih Li Sung, Wenjia Wang, Matthew Plumlee, Benjamin Haaland

Research output: Contribution to journalArticlepeer-review


The Gaussian process is a standard tool for building emulators for both determin-istic and stochastic computer experiments. However, application of Gaussian process models is greatly limited in practice, particularly for large-scale and many-input com-puter experiments that have become typical. We propose a multi-resolution functional ANOVA model as a computationally feasible emulation alternative. More generally, this model can be used for large-scale and many-input non-linear regression problems. An overlapping group lasso approach is used for estimation, ensuring computational feasibility in a large-scale and many-input setting. New results on consistency and inference for the (potentially overlapping) group lasso in a high-dimensional setting are developed and applied to the proposed multi-resolution functional ANOVA model. Importantly, these results allow us to quantify the uncertainty in our predictions. Numerical examples demonstrate that the proposed model enjoys marked compu-tational advantages. Data capabilities, both in terms of sample size and dimension, meet or exceed best available emulation tools while meeting or exceeding emulation accuracy.

Original languageEnglish (US)
JournalUnknown Journal
StatePublished - Sep 20 2017


  • Computer experiments
  • Large-scale
  • Many-input
  • Non-linear regression
  • Overlapping group lasso

ASJC Scopus subject areas

  • General

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