STATISTICS COLLOQUIUM
Mengyang Gu, PhD
Assistant Research Professor
Department of Applied Mathematics & Statistics
Johns Hopkins University
Robust calibration, emulation and scalable computation for imperfect mathematical models with massive output
Abstract
We focus on the problem of calibrating imperfect mathematical models using experimental data. To compensate for the misspecification of the mathematical model, a discrepancy function is usually included and modeled via a Gaussian stochastic process (GaSP), leading to better results of prediction. The calibrated mathematical model itself, however, sometimes fits the experimental data poorly, as the calibration parameters become unidentifiable. In this work, we propose the scaled Gaussian stochastic process (S-GaSP), a novel stochastic process for calibration and prediction. This new approach bridges the gap between two predominant methods, namely the L2 calibration and GaSP calibration. A computationally feasible approach is introduced for this new model under the Bayesian paradigm. New robust and computationally efficient statistical models will also be discussed for emulating computationally expensive mathematical models with massive output. The spatio-temporal outputs from TITAN2D, a computer model that simulates volcanic eruption, and the Interferometric synthetic aperture radar (InSAR) data will be used to demonstrate the performance of the proposed statistical methods for emulation and calibration.
DATE: Wednesday, March 21, 2018
TIME: 4:00 pm
PLACE: Philip E. Austin Bldg., Rm. 108
Coffee will be served at 3:30 pm in the Noether Lounge (AUST 326)
For more information, contact: Tracy Burke at tracy.burke@uconn.edu