Probabilistic modeling of discrete structural response with application to composite plate penetration models

Abstract

Discrete response of structures is often a key probabilistic quantity of interest. For example, one may need to identify the probability of a binary event, such as, whether a structure has buckled or not. In this study, an adaptive domain-based decomposition and classification method, combined with sparse grid sampling, is used to develop an efficient classification surrogate modeling algorithm for such discrete outputs. An assumption of monotonic behaviour of the output with respect to all model parameters, based on the physics of the problem, helps to reduce the number of model evaluations and makes the algorithm more efficient. As an application problem, this paper deals with the development of a computational framework for generation of probabilistic penetration response of S-2 glass/SC-15 epoxy composite plates under ballistic impact. This enables the computationally feasible generation of the probabilistic velocity response (PVR) curve or the V 0 − V 100 curve as a function of the impact velocity, and the ballistic limit velocity prediction as a function of the model parameters. The PVR curve incorporates the variability of the model input parameters and describes the probability of penetration of the plate as a function of impact velocity.

An efficient optimization based microstructure reconstruction approach with multiple loss functions

“…The methods [14,15,16,17] are mostly analytical in nature and hence much faster than the optimization-based reconstruction approaches but their application is restricted to isotropic binary materials. Another class of microstructure reconstruction methods involves using deep learning [18,19], a machine learning approach that can be used amongst other tasks for surrogate modeling [20,21,22,23,24,25,26] and thus has been implemented successfully for a wide range of classification and regression tasks. Deep learning approaches, in particular convolutional neural networks, are particularly well-suited to handle image data, and have thus received attention lately from the materials research community to process microstructures image data for a variety of tasks [27,28].…”