Surrogate Optimization
Author:
Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-62029-4_6
Reference37 articles.
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2. G. Teichert and K. Garikipati. Machine learning materials physics: Surrogate optimization and multi-fidelity algorithms predict precipitate morphology in an alternative to phase field dynamics. Computer Methods in Applied Mechanics and Engineering, 344:666–693, 2019.
3. M.E. Thompson and P.W. Voorhees. Equilibrium particle morphologies in elastically stressed coherent solids. Acta Materialia, 47(3):983–996, 1999.
4. S. Müller, C. Wolverton, L-W. Wang, and A. Zunger. Prediction of alloy precipitate shapes from first principles. Europhysics Letters, 55:33–39, 2001.
5. V. Vaithyanathan, C. Wolverton, and L.Q. Chen. Multiscale modeling of precipitate microstructure evolution. Physical Review Letters, 88:125503–1–125503–4, 2002.
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