Announcement
Thesis Defense: Anindya Bhaduri, “Adaptive Construction of Surrogate Functions for Various Computational Mechanics Models”

September 19, 2019

THE DEPARTMENT OF CIVIL ENGINEERING

AND

ADVISOR LORI GRAHAM-BRADY, PROFESSOR AND CHAIR

ANNOUNCE THE THESIS DEFENSE OF

Doctoral Candidate

Anindya Bhaduri

Friday, September 20, 2019

2:00PM

Latrobe 106

“Adaptive Construction of Surrogate Functions for Various Computational Mechanics Models”

Abstract:

In most science and engineering fields, numerical simulation models are often used to replicate physical systems. An attempt to imitate the true behavior of complex systems results in computationally expensive simulation models. The models are more often than not associated with a number of parameters that may be uncertain or variable. Propagation of variability from the input parameters in a simulation model to the output quantities is important for better understanding the system behavior. Variability propagation of complex systems requires repeated runs of costly simulation models with different inputs, which can be prohibitively expensive. Thus for efficient propagation, the total number of model evaluations needs to be as few as possible. An efficient way to account for the variations in the output of interest with respect to these parameters in such situations is to develop black-box surrogates. It involves replacing the expensive high-fidelity simulation model by a much cheaper model (surrogate) using a limited number of the high-fidelity simulations on a set of points called the design of experiments (DoE).

In this talk, a couple of examples related to newly developed adaptive black-box surrogate constructions are presented first to demonstrate the usefulness of surrogate construction in general. Then we consider an LS-DYNA simulation model of a continuum level plain weave S-2 glass/SC-15 epoxy composite plate under ballistic impact by a cylinder projectile. The goal is to develop an efficient computational framework for generation of probabilistic penetration response of the plate using surrogates. More specifically, this study involves construction of adaptive classification surrogates in order to generate two important quantities of interest, the probabilistic velocity response (PVR) curve as a function of the projectile impact velocity, and the ballistic limit velocity prediction as a function of the strength parameters of the plate model.

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