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Estimating kinetic energy reduction for terminal ballistics using a hyperparameter-optimized neural network

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Abstract

A coupled framework of ballistic simulations and an optimized machine learning (ML) model was developed to accurately predict the kinetic energy reduction of a projectile impacting a target. ML models can require a significant number of data points for proper training, testing, and validation. High-performance computing (HPC) resources can be used to simulate the ballistic impacts of various projectiles against several different target materials using appropriate physics-based hydrocodes. Computational modeling can explore areas where experiments would naturally be cost-prohibitive. These hydrocodes can evaluate large parametric spaces varying the projectile and target variables that are required to train an ML model. In this study a large, generated set of data points was used to develop an optimized artificial neural network (ANN) algorithm to create a fast-running model without prior knowledge of the mathematical relationships between all the input and output variables. The optimized ANN model was developed using Optuna in an HPC environment to tune the hyperparameters needed for the ANN model. This fast-running ML model could then be leveraged to investigate designing optimized targets that could protect against different types of projectiles. The results of this work showed that the optimized ANN model predicted the kinetic energy reduction with a mean absolute percentage error of 2.7% across the validation data. Overall, the optimized ANN model showed excellent agreement across the range of data considered by the computational models.

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Data Availability Statement

The datasets generated during and/or analyzed during the current study are not publicly available due to several pending publications but are available from the corresponding author on reasonable request.

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Acknowledgements

This research was conducted on behalf of the U.S. Army Engineer Research and Development Center’s (ERDC) Information Technology Laboratory and the Geotechnical Structures Laboratory. This work was supported in part by high-performance computer time and resources from the DoD High Performance Computing Modernization Program. This study was conducted for the ERDC Programs Office FLEX-4 Future Innovation Fund (FIF), “Physics Informed Machine Learning for ERDC Applications.” The technical monitor was Mr. William Ryder, ERDC Programs Office. Machine learning research was supported in part by the U.S. Army ERDC, Information Technology Laboratory, under PE 603465, Project AL3, “High-Performance Computing for Rotorcraft Applications Advanced Technologies.” The EPIC simulation research was conducted for the U.S. Army ERDC, Geotechnical and Structures Laboratory, under PE 633119, Project BL6, Task 02, “Force Protection in the Urban Environment (FPUE)”. Permission to publish this information was granted by the Directors, Information Technology Laboratory and Geotechnical Structures Laboratory.

Funding

This study was funded by the U.S. Army Engineer Research and Development Center’s Section 2363 FLEX-4 program under the Basic and Applied Research category. Additional, non-financial, support was provided by other projects under the U.S. Army Engineer Research and Development Center, including the High-Performance Computing Modernization Program (PE 0603461A), Force Protection in the Urban Environment (PE 633119/BL6), and High-Performance Computing for Rotorcraft Applications Advanced Technologies (PE 603465/AL3).

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Author notes

  1. Jesse Sherburn, James Ross, and Yi Zhang have contributed equally to this work.

Authors and Affiliations

  1. Information Technology Laboratory, U.S. Army Engineer Research and Development Center, 3909 Halls Ferry Road, Vicksburg, MS, 39180, USA

    Brianna Thompson, James Ross & Yi Zhang

  2. Geotechnical Structures Laboratory, U.S. Army Engineer Research and Development Center, 3909 Halls Ferry Road, Vicksburg, MS, 39180, USA

    Jesse Sherburn

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  1. Brianna Thompson
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Correspondence to Brianna Thompson.

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Thompson, B., Sherburn, J., Ross, J. et al. Estimating kinetic energy reduction for terminal ballistics using a hyperparameter-optimized neural network. Neural Comput & Applic 36, 6531–6545 (2024). https://doi.org/10.1007/s00521-023-09382-3

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