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In phase-field simulations, accurate material parameters are required to quantitatively predict microstructural evolutions. Non-sequential data assimilations enable the estimation of unknown material parameters by minimizing a cost function that represents the misfit between numerical simulation results and time-series observation data. In this study, a new non-sequential data assimilation method Minimizing the Cost function using tree-structured Parzen estimator (TPE), namely DMC-TPE, with higher estimation accuracy than that of a conventional method (DMC-Bayesian optimization (BO)) is developed. The estimation accuracy of DMC-TPE is compared with that of DMC-BO via numerical experiments, where these methods are applied to a phase-field simulation of solid-state sintering. The comparison results demonstrate that the estimation accuracy of DMC-TPE is higher than that of DMC-BO, specifically in cases where numerous parameters have to be estimated, because TPE can continuously minimize the cost function by increasing the number of iterative minimization calculations. Furthermore, DMC-TPE provides less scatter in the estimation results than that in the case of DMC-BO. The DMC-TPE developed herein leads to highly accurate PF simulations of microstructural evolution by simultaneously estimating the states and many unknown material parameters with high accuracies based on experimental data.

権利情報:

• Creative Commons BY Attribution 4.0 International
  

キーワード: Data assimilation, Bayesian optimization, Phase-field simulation, Sintering

刊行年月日: 2023-12-31

出版者: Informa UK Limited

掲載誌:

• Science and Technology of Advanced Materials: Methods (ISSN: 27660400) vol. 3 issue. 1 2239133

研究助成金:

• Strategic Basic Research Programs
• Core Research for Evolutional Science and Technology JPMJCR18J4

原稿種別: 出版者版 (Version of record)

MDR DOI:

公開URL: https://doi.org/10.1080/27660400.2023.2239133

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更新時刻: 2026-08-27 16:27:13 +0900

MDRでの公開時刻: 2026-08-27 18:27:16 +0900