Workshop on Computer Experiments

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1. Opening: Overview on Computer Experiments

   Speaker: Professor Jeff Wu (ISyE, Gatech)


2. Title: Multi-Layer Designs for Computer Experiments

   Speaker: Professor Roshan J. Vengazhiyil (ISyE, Gatech)

Abstract: Computer experiments play a major role in the modern era of scientific and technological development. In designing computer experiments, Latin hypercube designs (LHDs) are widely used. However, finding an optimal LHD is computationally cumbersome. On the other hand, although many optimal designs are well known for physical experiments, the redundancy of design points make them undesirable for computer experiments. In this work, we present a new class of space-filling designs developed by splitting two-level full or fractional factorial designs into multiple layers. The method takes advantages of many available results in designing physical experiments and therefore, the proposed Multi-layer designs (MLDs) are easy to generate. Moreover, our numerical study shows that MLDs can have better space-filling properties than optimal LHDs.


3. Title: Some New Advances in Design and Modeling of Computer Experiments

Speaker: Professor Peter Z. G. Qian (Statistics, Wisconsin)

Abstract: Computer models are now becoming ubiquitous in nearly all fields of sciences and engineering. Design and modeling are two key aspects of computer experiments. In this talk, I will report some recent advances in both aspects. Specific topics include a new approach for emulation of computer models with qualitative and quantitative factors; sequential space-filling designs; Sudoku based space-filling designs; and sliced Latin hypercube designs for ensembles of computer models. 


4. Title: Analysis of Computer Experiments with Functional Response

   Speaker: Professor Ying Hung  (Statistics, Rutgers)

Abstract: Most existing methods for analyzing computer experiments with single outputs such as kriging cannot be easily applied to functional outputs due to the computational problems caused by high-dimensionality of the response. In this paper, we develop an efficient implementation of kriging for analyzing functional responses. Our methodology uses a two-stage model building procedure with Kronecker products and an improved EM algorithm for estimation. The methodology is illustrated using a computer experiment conducted for optimizing residual stresses in machined parts. This is a joint work with V. Roshan Joseph and Shreyes N. Melkote.


  • Workflow Status:Published
  • Created By:Anita Race
  • Created:03/22/2010
  • Modified By:Fletcher Moore
  • Modified:10/07/2016


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