一般講演

[G] Data Science

Wed. Sep 21, 2022 3:05 PM - 5:00 PM Rm. E (D24,2Flr. Build.D)

座長:北嶋　具教(物質・材料研究機構)、小山敏幸(名古屋大学)

※表示の講演時間には質疑応答時間も含みます。
（質疑応答時間5分、基調講演と招待講演は5～10分）

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3:05 PM - 3:20 PM

[109] Parameter estimation for fluid flow based on data assimilation

*Kazuto Moriguchi¹, Ryo Yamada¹, Munekazu Ohno¹ (1. Hokkaido Univ.)

3:20 PM - 3:35 PM

[110] Development of machine learning model for inverse analysis of anisotropy strength of interfacial energy from microsegregation

*Souta FUKUZAWA¹, Ryo Yamada², Munekazu Ohno² (1. Graduated school, Hokkaido Univ, 2. Hokkaido Univ)

3:35 PM - 3:50 PM

[111] Search for high creep strength welding conditions considering HAZ shape factor

*Hitoshi IZUNO¹, Masahiko DEMURA¹, Satoshi MINAMOTO¹, Junya Sakurai¹, Masayoshi YAMAZAKI¹, Kenji NAGATA¹, Yoh-ichi MOTOTAKE², Daisuke ABE³, Keisuke TORIGATA³ (1. NIMS, 2. The Inst of Statistical Mathematics, 3. IHI)

3:50 PM - 4:05 PM

[112] Non-Isothermal Heat Treatments designed by Artificial Intelligence for Ni/Ni3Al two-phase alloys

*Vickey NANDAL¹, Sae DIEB¹, Dmitry　S. BULGAREVICH¹, Toshio OSADA¹, Toshiyuki KOYAMA², Masahiko DEMURA¹ (1. NIMS, 2. Nagoya University)

break （4:05 PM - 4:15 PM）

4:15 PM - 4:30 PM

[113] Deep learning TEM image segmentation for automated microstructural analysis of FePt-C recording media

*Nikita Kulesh¹, Anton Bolyachkin¹, Ippei Suzuki¹, Yukiko Takahashi¹, Hossein Sepehri-Amin¹ (1. NIMS)

4:30 PM - 4:45 PM

[114] Evaluation of Tensile Strength based on Microscopic Imaging Data of Steel
by Mathematical Approaches

*Kazuto Akagi¹, Ippei Obayashi², Yasuaki Hiraoka^1,3,4, Yasumasa Nishiura^1,5 (1. Tohoku Univ., 2. Okayama Univ., 3. Kyoto Univ., 4. Riken, 5. Hokkaido Univ.)

4:45 PM - 5:00 PM

[115] Estimation of material microstructure images by combining deep learning framework and phase transformation model

*Satoshi NOGUCHI¹, Junya Inoue² (1. Eng. Univ. Tokyo, 2. IIS Univ. Tokyo)

Sessions

11.Computational Science » Data Science

[G] Data Science

[109] Parameter estimation for fluid flow based on data assimilation

[110] Development of machine learning model for inverse analysis of anisotropy strength of interfacial energy from microsegregation

[111] Search for high creep strength welding conditions considering HAZ shape factor

[112] Non-Isothermal Heat Treatments designed by Artificial Intelligence for Ni/Ni3Al two-phase alloys

[113] Deep learning TEM image segmentation for automated microstructural analysis of FePt-C recording media

[114] Evaluation of Tensile Strength based on Microscopic Imaging Data of Steel
by Mathematical Approaches

[115] Estimation of material microstructure images by combining deep learning framework and phase transformation model

Authentication

Sessions

11.Computational Science » Data Science

[G] Data Science

[109] Parameter estimation for fluid flow based on data assimilation

[110] Development of machine learning model for inverse analysis of anisotropy strength of interfacial energy from microsegregation

[111] Search for high creep strength welding conditions considering HAZ shape factor

[112] Non-Isothermal Heat Treatments designed by Artificial Intelligence for Ni/Ni3Al two-phase alloys

[113] Deep learning TEM image segmentation for automated microstructural analysis of FePt-C recording media

[114] Evaluation of Tensile Strength based on Microscopic Imaging Data of Steelby Mathematical Approaches

[115] Estimation of material microstructure images by combining deep learning framework and phase transformation model

Authentication

[114] Evaluation of Tensile Strength based on Microscopic Imaging Data of Steel
by Mathematical Approaches