• Open Access

Bayesian inference of grain growth prediction via multi-phase-field models

Shin-ichi Ito, Hiromichi Nagao, Takashi Kurokawa, Tadashi Kasuya, and Junya Inoue
Phys. Rev. Materials 3, 053404 – Published 30 May 2019

Abstract

We propose a Bayesian inference methodology to evaluate unobservable parameters involved in multi-phase-field models to accurately predict the observed grain growth, such as in metals and alloys. This approach integrates models and a set of observational image data of grain structures. Because the image data set is not a time series, directly applying conventional inference techniques that require time series as the input data is difficult. The key idea in our methodology to overcome this difficulty is to construct a time series with an appropriate statistic that characterizes static image data of grain structures. Our methodology implements the empirical Bayes method. It can estimate not only a probability density function of the parameters but also an initial phase field, which is generally unobservable in real experiments. After validating the proposed method through numerical tests using synthetic data, we apply it to real experimental images of grain structures in a steel alloy. The proposed method properly estimates unobservable parameters along with their uncertainties and successfully selects the initial phase field that best explains the experimental data from among candidate initial phase fields.

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  • Received 18 September 2018

DOI:https://doi.org/10.1103/PhysRevMaterials.3.053404

Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

©2019 American Physical Society

Physics Subject Headings (PhySH)

  1. Research Areas
  1. Physical Systems
Condensed Matter, Materials & Applied Physics

Authors & Affiliations

Shin-ichi Ito1, Hiromichi Nagao1,2, Takashi Kurokawa2,*, Tadashi Kasuya3, and Junya Inoue4,3

  • 1Earthquake Research Institute, The University of Tokyo, 1-1-1, Yayoi, Bunkyo-ku, Tokyo 113-0032, Japan
  • 2Graduate School of Information Science and Technology, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo 113-8656, Japan
  • 3Graduate School of Engineering, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo 113-8656, Japan
  • 4Research Center for Advanced Science and Technology, The University of Tokyo, 4-6-1, Komaba, Meguro-ku, Tokyo 153-8904, Japan

  • *Present address: Yahoo Japan Corporation, Japan.

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Issue

Vol. 3, Iss. 5 — May 2019

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