日本地球惑星科学連合2019年大会

講演情報

[E] ポスター発表

セッション記号 M (領域外・複数領域) » M-GI 地球科学一般・情報地球科学

[M-GI30] Data assimilation: A fundamental approach in geosciences

2019年5月29日(水) 17:15 〜 18:30 ポスター会場 (幕張メッセ国際展示場 8ホール)

コンビーナ:中野 慎也(情報・システム研究機構 統計数理研究所)、藤井 陽介(気象庁気象研究所)、宮崎 真一(京都大学理学研究科)、三好 建正(理化学研究所)

[MGI30-P06] Bayesian inference of grain growth prediction via multi-phase-field models

*伊藤 伸一1長尾 大道1黒河 天1糟谷 正1井上 純哉1 (1.東京大学)

キーワード:粒成長、ベイズ推論、データ同化、フェーズフィールドモデル

We propose a Bayesian inference methodology to evaluate unobservable parameters involved in multi-phase-field models with the aim of accurately predicting the observed grain growth, such as in metals and rocks. This approach integrates models and a set of observational image data of grain structures. Since the set of image data 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 together with their uncertainties, and successfully selects the initial phase-field that best explains the experimental data from among candidate initial phase-fields.