Developing a robust strength model using physically-informed genetic programming

Aragon, Nicole K.; Lim, Hojun; Battaile, Corbett Chandler; Lane, J. Matthew D.; de Oca Zapiain, David Montes

doi:10.1016/j.commatsci.2025.113959

Developing a robust strength model using physically-informed genetic programming

Journal Article · Fri Jun 13 00:00:00 EDT 2025 · Computational Materials Science

DOI:https://doi.org/10.1016/j.commatsci.2025.113959· OSTI ID:2585659

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Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

The strength of materials is influenced by a range of external conditions, such as temperature and deformation rate. Consequently, materials that demonstrate substantial variations in their mechanical behavior due to fluctuations in temperature and strain rate require complex strength models to accurately predict material performance in real-world applications. To predict such complex behavior, a robust and flexible strength model is necessary. In this work, we utilize genetic programming-based symbolic regression (GPSR) to develop data-driven strength models that accurately represent the measured stress–strain responses of tin across a wide range of strain, strain rate and temperature regimes. The GPSR models are constrained by physically-informed conditions, which leads to significant improvement in extrapolation. The best model is integrated into a multi-physics code to perform Taylor impact simulations, validating the model’s accuracy and robustness. In conclusion, the model predictions showed excellent agreement with experimental results, particularly when compared to predictions using traditional strength models.

View Accepted Manuscript (DOE)

Research Organization:: Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: NA0003525

OSTI ID:: 2585659

Report Number(s):: SAND--2025-08718J; 1764659

Journal Information:: Computational Materials Science, Journal Name: Computational Materials Science Vol. 258; ISSN 0927-0256

Publisher:: Elsevier BVCopyright Statement

Country of Publication:: United States

Language:: English

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Physically-informed modeling
Strength
Symbolic regression
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