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

arXiv: https://arxiv.org/abs/2011.11780 · HTML: https://mlsanity.com/paper/2011.11780/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Nov 2020.

## Built on by (most cited first, 5 of 5)

- [Stress field prediction in fiber-reinforced composite materials using a deep learning approach](https://mlsanity.com/paper/2111.05271/) (2021)
- [An efficient optimization based microstructure reconstruction approach with multiple loss functions](https://mlsanity.com/paper/2102.02407/) (2021)
- [Physics-Informed Graph Neural Networks to Reconstruct Local Fields Considering Finite Strain Hyperelasticity](https://mlsanity.com/paper/2507.05291/) (2025)
- [Application of probabilistic modeling and automated machine learning framework for high-dimensional stress field](https://mlsanity.com/paper/2303.16869/) (2023)
- [Non-linear mechanical field reconstruction coupling recurrent neural networks with physics-informed graph neural networks](https://mlsanity.com/paper/2606.10909/) (2026)

## Abstract (arXiv)

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 V0-V100 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.