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TITLE: Analysis of Large-Scale Computer Experiments
SPEAKER: Lulu Kang
PhD Candidate (in the Statistics Program)
School of Industrial and Systems Engineering
Georgia Tech
ABSTRACT:
Computer experiments simulate the engineering systems by
implementing the mathematical models governing the systems in computers.
Recently, experiments having large number of input variables and
experimental runs started to emerge. In the existing literature, kriging
has been commonly used for approximating the complex computer models,
but it has limitations for dealing with the large-scale experiments due
to its computational complexity and numerical stability. In this work, I
propose a new modeling approach known as regression-based inverse
distance weighting (RIDW). The new predictor is shown to be
computationally more efficient than kriging while producing comparable
prediction performance. We also develop a heuristic method for
constructing confidence intervals for prediction. I will also discuss
extensions of RIDW and my future research directions on this exciting topic
Bio:
Lulu Kang is a Ph.D. candidate in the Statistics Program of the School
of Industrial and Systems Engineering at Georgia Institute of
Technology. She is working with Professor Roshan J. Vengazhiyil. Her
research interests are in developing statistical theories and
methodologies, as well as their applications in physical science and
engineering.
