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Developing a robust strength model using physically-informed genetic programming

delete2025-06-13
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PRE
AI
N
Nicole K. Aragon
H
Hojun Lim
C
Corbett Chandler. Battaile
J
J. Matthew D. Lane
D
David Montes de Oca Zapiain
DOI:10.1016/j.commatsci.2025.113959delete
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Abstract

Abstract

En 中文
• Genetic programming (GPSR) is used to generate a data-driven strength model • GPSR models require physically-informed constraints to predict realistic behavior • Predictions from the developed strength model show good agreement with experiments

Journal

Computational Materials Science cover
Computational Materials Science
IF:
3.3
Papers:
1.4W
Citations:
4.0W

Organization

No organization information available
Cited Papers

Cited Papers

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errGarbrecht, Karl; Birky, Donovan; Lester, Brian; Emery, John; Hochhalter, Jacob
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Symbolic regression in materials science
err2019-09-20
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errYiqun Wang; Nicholas Wagner; James M. Rondinelli
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Toward Automated Design of Industrial-Strength Analog Circuits by Means of Genetic Programming
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err2024-09-04
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errJohn R. Koza; Lee W. Jones; Martin A. Keane; Matthew J. Streeter; Sameer H. Al-Sakran
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