Mototake Yoh-ichi 本武陽一

Researcher Number	80848672
Other IDs
Affiliation (Current)	2025: 一橋大学, 大学院ソーシャル・データサイエンス研究科, 准教授
Affiliation (based on the past Project Information) *help	2023 – 2025: 一橋大学, 大学院ソーシャル・データサイエンス研究科, 准教授 2022: 一橋大学, ソーシャル・データサイエンス研究科, 准教授 2020 – 2021: 統計数理研究所, 統計的機械学習研究センター, 特任助教
Review Section/Research Field	Principal Investigator Transformative Research Areas, Section (II) / Basic Section 13010:Mathematical physics and fundamental theory of condensed matter physics-related / Science and Engineering Except Principal Investigator Medium-sized Section 61:Human informatics and related fields / Sections That Are Subject to Joint Review: Basic Section60030:Statistical science-related , Basic Section61030:Intelligent informatics-related / Basic Section 60030:Statistical science-related / Basic Section 61030:Intelligent informatics-related / Medium-sized Section 16:Astronomy and related fields
Keywords	Principal Investigator 解釈可能AI / パターンダイナミクス / 深層ニューラルネット / 対称性推定 / 保存量推定 / 深層ニューラルネットワーク / 準安定状態の特徴量 / 逆磁区発生の機序 / ブロックコポリマーの相分離構造 / パーシステントホモロジー … More / パターン形成 / 位相幾何的データ分析 / パターン形成過程 / 位相的データ分析 / 高分子ポリマー / 磁区構造 / 強磁性体 / 機械学習 / 位相的データ解析 … More Except Principal Investigator 適応・進化 / 自己組織化 / 計算トポロジー / アクティブマター / ロバスト推定 / 統計的データ解析 / 機械学習 / 多様体 / 確率密度関数 / 位相的データ解析 / 幾何学的データ / 統計計算・コンピュータ支援統計 / 統計的学習理論 / 宇宙論 / 銀河形成・進化 / 理論天文学 Less

位相的データ解析による階層構造をもつ大規模群れ運動の縮約モデリングとその応用Principal Investigator
- Principal Investigator
  
  本武陽一
- Project Period (FY)
  2025 – 2026
- Research Category
  
  Grant-in-Aid for Transformative Research Areas (A)
- Review Section
  
  Transformative Research Areas, Section (II)
- Research Institution
  Hitotsubashi University
Machine-Learning-Reinforced Cosmic Structure Formation: From Large-Scale Structure Formation to Galaxy Evolution
- Principal Investigator
  
  竹内努
- Project Period (FY)
  2024 – 2028
- Research Category
  
  Grant-in-Aid for Scientific Research (A)
- Review Section
  
  Medium-sized Section 16:Astronomy and related fields
- Research Institution
  Nagoya University
Constructing Induced self-organization in active matters using computational topology
- Principal Investigator
  
  末谷大道
- Project Period (FY)
  2024 – 2025
- Research Category
  
  Grant-in-Aid for Challenging Research (Exploratory)
- Review Section
  
  Medium-sized Section 61:Human informatics and related fields
- Research Institution
  Oita University
幾何学的データ解析手法の開発と位相的データ解析への展開
- Principal Investigator
  
  佐々木博昭
- Project Period (FY)
  2023 – 2027
- Research Category
  
  Grant-in-Aid for Scientific Research (B)
- Review Section
  
  Basic Section 61030:Intelligent informatics-related
  Basic Section 60030:Statistical science-related
  Sections That Are Subject to Joint Review: Basic Section60030:Statistical science-related , Basic Section61030:Intelligent informatics-related
- Research Institution
  Meiji Gakuin University
  Future University-Hakodate
Development of machine learning methods for discovering symmetries in pattern dynamicsPrincipal Investigator
- Principal Investigator
  
  本武陽一
- Project Period (FY)
  2022 – 2026
- Research Category
  
  Grant-in-Aid for Early-Career Scientists
- Review Section
  
  Basic Section 13010:Mathematical physics and fundamental theory of condensed matter physics-related
- Research Institution
  Hitotsubashi University
Constructing a reduced model of a pattern formation process on the basis of topological data analysisPrincipal Investigator
- Principal Investigator
  
  本武陽一
- Project Period (FY)
  2020 – 2021
- Research Category
  
  Grant-in-Aid for Scientific Research on Innovative Areas (Research in a proposed research area)
- Review Section
  
  Science and Engineering
- Research Institution
  The Institute of Statistical Mathematics

All 2024 2023 2022 2021 2020

All Journal Article Presentation

[Journal Article] Autoregressive With Slack Time Series Model for Forecasting a Partially-Observed Dynamical Time Series2024
- Author(s)
  Akifumi Okuno, Yuya Morishita, Yoh-ichi Mototake
- Journal Title
  
  IEEE Access
  
  Volume: 12 Pages: 24621-24630
- DOI
  10.1109/access.2024.3365724
- Peer Reviewed / Open Access
- Data Source
  KAKENHI-PROJECT-21K17718, KAKENHI-PROJECT-22K13979, KAKENHI-PROJECT-23K28150
[Journal Article] Quantifying physical insights cooperatively with exhaustive search for Bayesian spectroscopy of X-ray photoelectron spectra2023
- Author(s)
  Hiroyuki Kumazoe, Kazunori Iwamitsu, Masaki Imamura, Kazutoshi Takahashi, Yoh-ichi Mototake, Masato Okada, Ichiro Akai
- Journal Title
  
  Scientific Reports
  
  Volume: 13 Issue: 1
- DOI
  10.1038/s41598-023-40208-3
- Peer Reviewed / Open Access
- Data Source
  KAKENHI-PROJECT-22K13979, KAKENHI-PROJECT-23H00486
[Journal Article] Revealing the Mechanism of Large-scale Gradient Systems Using a Neural Reduced Potential2023
- Author(s)
  Shunya Tsuji, Ryo Murakami, Hayaru Shouno, Yoh-ichi Mototake
- Journal Title
  
  NeurIPS2023 Workshop on Machine Learning and the Physical Sciences(ML4PS)
  
  Volume: -
- Peer Reviewed / Open Access
- Data Source
  KAKENHI-PROJECT-22K13979
[Journal Article] Extracting Nonlinear Symmetries From Trained Neural Networks on Dynamics Data2023
- Author(s)
  Yoh-ichi Mototake
- Journal Title
  
  NeurIPS2023 Workshop on AI for Sciences: from Theory to Practice
  
  Volume: -
- Peer Reviewed / Open Access
- Data Source
  KAKENHI-PROJECT-22K13979
[Journal Article] 位相的データ解析による銀河分布の定量化とバリオン音響振動抽出2023
- Author(s)
  竹内努，河野海，クレスチェータ，西澤淳，村上広耶，馬海霞，本武陽一
- Journal Title
  
  統計数理
  
  Volume: 71(2) Pages: 159-187
- Peer Reviewed / Open Access
- Data Source
  KAKENHI-PROJECT-22K13979
[Journal Article] Quantitative prediction of fracture toughness (KIc) of polymer by fractography using deep neural networks2022
- Author(s)
 Mototake Y.、Ito K.、Demura M.
- Journal Title
 
 Science and Technology of Advanced Materials: Methods
 
 Volume: 2 Issue: 1 Pages: 310-321
- DOI
 10.1080/27660400.2022.2107883
- Peer Reviewed / Open Access
- Data Source
 KAKENHI-PROJECT-22K13979
[Journal Article] Quantitative Prediction of Fracture Toughness (KIc) of Polymer by Fractography Using Deep Neural Networks2022
- Author(s)
  Yoh-ichi Mototake, Kaita Ito, Masahiko Demura
- Journal Title
  
  arxiv
  
  Volume: -
- Open Access
- Data Source
  KAKENHI-PUBLICLY-20H04648
[Journal Article] Revealing the Mechanism of Magnetic Domain Formation by Topological Data Analysis2022
- Author(s)
  Yoh-ichi Mototake, Masaichiro Mizumaki, Kazue Kudo, Kenji Fukumizu
- Journal Title
  
  arxiv
  
  Volume: 2204.12194
- Data Source
  KAKENHI-PROJECT-22K13979
[Journal Article] Revealing the Mechanism of Magnetic Domain Formation by Topological Data Analysis2022
- Author(s)
  Yoh-ichi Mototake, Masaichiro Mizumaki, Kazue Kudo, Kenji Fukumizu
- Journal Title
  
  arxiv
  
  Volume: -
- Open Access
- Data Source
  KAKENHI-PUBLICLY-20H04648
[Journal Article] Interpretable conservation law estimation by deriving the symmetries of dynamics from trained deep neural networks2021
- Author(s)
  Mototake Yoh-ichi
- Journal Title
  
  Physical Review E
  
  Volume: 103 Issue: 3 Pages: 033303-033303
- DOI
  10.1103/physreve.103.033303
- Peer Reviewed / Open Access
- Data Source
  KAKENHI-PUBLICLY-20H04648
[Journal Article] 位相的データ分析法による材料構造形成過程の分析2021
- Author(s)
  本武陽一、水牧仁一朗、工藤和恵、福水健次
- Journal Title
  
  スマートプロセス学会誌
  
  Volume: 10(3) Pages: 108-120
- Peer Reviewed
- Data Source
  KAKENHI-PUBLICLY-20H04648
[Journal Article] Free Energy Estimation of Metastable Structures of Block Copolymers using Topological Data Analysis2020
- Author(s)
  本武陽一、山中貞人、青柳岳司、大西立顕、福水健次
- Journal Title
  
  J. Comput. Chem. Jpn.
  
  Volume: 19 Issue: 4 Pages: 169-171
- DOI
  10.2477/jccj.2021-0009
- NAID
  130008031793
- ISSN
  1347-1767, 1347-3824
- Language
  Japanese
- Peer Reviewed / Open Access
- Data Source
  KAKENHI-PLANNED-17H06464, KAKENHI-PUBLICLY-20H04648, KAKENHI-ORGANIZER-17H06460
[Journal Article] Towards a Geometrical Understanding of Physical Phenomena via Extraction of Data Manifolds using Generative Models2020
- Author(s)
  Kotaro Sakamoto、Yuichiro Mori、Yoh-ichi Mototake
- Journal Title
  
  Proceedings of the 2020 International Symposium on Nonlinear Theory and its Applications
  
  Volume: 2020 Pages: 255-255
- Peer Reviewed
- Data Source
  KAKENHI-PUBLICLY-20H04648
[Journal Article] Topological Data Analysis for microdomain patternsof Block Copolymer2020
- Author(s)
  Yoh-ichi Mototake、Sadato Yamanaka、Takeshi Aoyagi、Takaaki Ohnishi、Kenji Fukumizu
- Journal Title
  
  Proceedings of the 2020 International Symposium on Nonlinear Theory and its Applications
  
  Volume: 2020 Pages: 517-517
- Peer Reviewed
- Data Source
  KAKENHI-PUBLICLY-20H04648
[Presentation] Interpretable reduced modeling of large-scale pattern dynamics - from materials science to astrophysics -2024
- Author(s)
  Yoh-ichi Mototake
- Organizer
  Statistical Analysis of Random Fields in Cosmology
- Invited / Int'l Joint Research
- Data Source
  KAKENHI-PROJECT-23K28150
[Presentation] Interpretable reduced modeling of large-scale pattern dynamics - from materials science to astrophysics -2024
- Author(s)
  You-ichi Mototake
- Organizer
  International Workshop KEK-Cosmo 2024
- Invited / Int'l Joint Research
- Data Source
  KAKENHI-PROJECT-22K13979
[Presentation] A Trial for the Realization of Material Pattern Informatics Using Interpretable AI2023
- Author(s)
  Yoh-ichi Mototake
- Organizer
  ICIAM Tokyo 2023 Minisymposium "Perspectives in Artificial Intelligence and Machine Learning in Materials Chemistry"
- Invited / Int'l Joint Research
- Data Source
  KAKENHI-PROJECT-22K13979
[Presentation] 解釈可能AIによるデータ駆動理学の実現へ向けた取り組み2023
- Author(s)
  本武　陽一
- Organizer
  第38回情報計測オンラインセミナー
- Invited
- Data Source
  KAKENHI-PROJECT-23K28150
[Presentation] Neural reduced potentialによる勾配系の解析枠組みの提案2023
- Author(s)
  辻駿哉、村上諒、庄野逸、本武陽一
- Organizer
  2023年度人工知能学会全国大会（第37回）
- Data Source
  KAKENHI-PROJECT-23K28150
[Presentation] Interpretable AI supporting scientists' insight into large-scale dynamics2023
- Author(s)
  You-ichi Mototake
- Organizer
  Global Plasma Forum in Aomori 2023
- Invited / Int'l Joint Research
- Data Source
  KAKENHI-PROJECT-22K13979
[Presentation] Revealing the Mechanism of Large-scale Gradient Systems Using a Neural Reduced Potential2023
- Author(s)
  Shunya Tsuji, Ryo Murakami, Hayaru Shouno, Yoh-ichi Mototake
- Organizer
  NeurIPS 2023 Workshop on Machine Learning and the Physical Sciences(ML4PS)
- Int'l Joint Research
- Data Source
  KAKENHI-PROJECT-23K28150
[Presentation] 解釈可能AIによるデータ駆動理学の実現へ向けた取り組み2023
- Author(s)
  本武陽一
- Organizer
  第38回情報計測オンラインセミナー
- Invited
- Data Source
  KAKENHI-PROJECT-22K13979
[Presentation] Transformerを用いたフラクトグラフィ2023
- Author(s)
  山中翔太，有竹俊光，天本義史，本武陽一
- Organizer
  第26回情報論的学習理論ワークショップ
- Data Source
  KAKENHI-PROJECT-22K13979
[Presentation] Transformerを用いたフラクトグラフィ2023
- Author(s)
  山中翔太、有竹俊光、天本義史、本武陽一
- Organizer
  情報論的学習理論ワークショップIBIS
- Data Source
  KAKENHI-PROJECT-23K28150
[Presentation] Neural reduced potentialによる勾配系の解析枠組みの提案2023
- Author(s)
  辻駿哉，村上諒，庄野逸，本武陽一
- Organizer
  2023年度人工知能学会全国大会（第37回）
- Data Source
  KAKENHI-PROJECT-22K13979
[Presentation] Perspectives in Artificial Intelligence and Machine Learning in Materials Chemistry2023
- Author(s)
  Yoh-ichi Mototake
- Organizer
  ICIAM Tokyo 2023 Minisymposium
- Invited / Int'l Joint Research
- Data Source
  KAKENHI-PROJECT-23K28150
[Presentation] Extracting Nonlinear Symmetries From Trained Neural Networks on Dynamics Data2023
- Author(s)
  Yoh-ichi Mototake
- Organizer
  NeurIPS 2023 Workshop on AI for Sciences: from Theory to Practice
- Int'l Joint Research
- Data Source
  KAKENHI-PROJECT-23K28150
[Presentation] Interpretable AI Supporting Scientists' Insight into Large-Scale Dynamics2023
- Author(s)
  Yoh-ichi Mototake
- Organizer
  Global Plasma Forum in Aomori 2023
- Invited / Int'l Joint Research
- Data Source
  KAKENHI-PROJECT-23K28150
[Presentation] Deriving the nonlinear symmetries of dynamics from trained deep neural networks2022
- Author(s)
  Yoh-ichi Mototake
- Organizer
  APS April Meeting 2022
- Int'l Joint Research
- Data Source
  KAKENHI-PUBLICLY-20H04648
[Presentation] 材料パターン情報学へ向けた位相的データ解析による取り組み2022
- Author(s)
  本武陽一
- Organizer
  CRESTさきがけクラスター会議
- Invited
- Data Source
  KAKENHI-PROJECT-22K13979
[Presentation] 機械学習による時系列データ多様体に隠れた対称性抽出法とルンゲ・レンツベクトルの推定2022
- Author(s)
  本武陽一
- Organizer
  日本物理学会2022年次大会
- Data Source
  KAKENHI-PUBLICLY-20H04648
[Presentation] 磁区パターン形成過程の位相的データ解析2022
- Author(s)
  本武陽一
- Organizer
  第236回研究会／第69回化合物新磁性材料専門研究会
- Invited
- Data Source
  KAKENHI-PUBLICLY-20H04648
[Presentation] データサイエンスと科学のこころ2022
- Author(s)
  本武陽一
- Organizer
  機械学会関東支部茨城ブロックなるほど技術者講演会
- Invited
- Data Source
  KAKENHI-PROJECT-22K13979
[Presentation] 高分子の材料パターン情報学へ向けた位相的データ解析による取り組み2022
- Author(s)
  本武陽一
- Organizer
  京都大学化学研究所　高分子セミナー
- Invited
- Data Source
  KAKENHI-PROJECT-22K13979
[Presentation] 位相的データ解析の材料科学への応用事例紹介2022
- Author(s)
  本武陽一
- Organizer
  2021年度第3回ORセミナー
- Invited
- Data Source
  KAKENHI-PUBLICLY-20H04648
[Presentation] 位相的データ解析による磁区構造形成過程の機序解明2022
- Author(s)
  本武陽一
- Organizer
  日本物理学会春季大会
- Data Source
  KAKENHI-PROJECT-22K13979
[Presentation] Interpretation of trained deep neural networks to collaborate with scientists2021
- Author(s)
  Yoh-ichi Mototake
- Organizer
  Materials Research Meeting 2021
- Invited / Int'l Joint Research
- Data Source
  KAKENHI-PUBLICLY-20H04648
[Presentation] Quantitative Prediction of Fracture Toughness (K1c) of Polymer via Fractography using Deep Neural Networks2021
- Author(s)
  Yoh-ichi Mototake
- Organizer
  Materials Research Meeting 2021
- Int'l Joint Research
- Data Source
  KAKENHI-PUBLICLY-20H04648
[Presentation] 力学系データで訓練された深層ニューラルネットからの非線形な対称性の抽出2021
- Author(s)
  本武陽一
- Organizer
  日本物理学会2021秋期大会
- Data Source
  KAKENHI-PUBLICLY-20H04648
[Presentation] Topological data analysis of pattern dynamics in material science2021
- Author(s)
  Yoh-ichi Mototake
- Organizer
  The 21st International Conference on Discrete Geometric Analysis for Materials Design
- Invited / Int'l Joint Research
- Data Source
  KAKENHI-PUBLICLY-20H04648
[Presentation] 位相的データ分析法による材料構造形成過程の分析2021
- Author(s)
  本武陽一
- Organizer
  九大先導研・新学術「材料離散幾何解析」合同シンポジウムマテリアルズインフォマティクス講演会
- Invited
- Data Source
  KAKENHI-PUBLICLY-20H04648
[Presentation] 位相的データ分析法による材料構造形成過程の分析2021
- Author(s)
  本武陽一
- Organizer
  統計物理と統計科学のセミナー
- Data Source
  KAKENHI-PUBLICLY-20H04648
[Presentation] TDAによる強磁性体磁区パターン形成過程の分析2020
- Author(s)
  本武陽一
- Organizer
  応用のためのトポロジカルデータ解析チュートリアル＆ワークショップ
- Data Source
  KAKENHI-PUBLICLY-20H04648
[Presentation] 物理学者と学習機械の効果的な協業に向けて：学習済み深層ニューラルネットワークからの解釈可能な物理法則抽出2020
- Author(s)
  本武陽一
- Organizer
  Deep learning and Physics 2020
- Invited
- Data Source
  KAKENHI-PUBLICLY-20H04648
[Presentation] 学習済み深層ニューラルネットワークからの解釈可能な物理法則抽出2020
- Author(s)
  本武陽一
- Organizer
  第14回　物性科学領域横断研究会 (領域合同研究会)
- Data Source
  KAKENHI-PUBLICLY-20H04648
[Presentation] 位相的データ分析による強磁性体磁区パターン形成過程の分析2020
- Author(s)
  本武陽一、水牧仁一朗、工藤和恵、福水健次
- Organizer
  TDA-MI workshop 2020
- Data Source
  KAKENHI-PUBLICLY-20H04648
[Presentation] 位相幾何的データ分析によるブロックコポリマー準安定構造の自由エネルギー推定2020
- Author(s)
  本武陽一、山中貞人、青柳岳司、大西立顕、福水健次
- Organizer
  日本コンピュータ化学会
- Data Source
  KAKENHI-PUBLICLY-20H04648
[Presentation] 位相的データ分析による強磁性体磁区パターン形成過程の分析2020
- Author(s)
  本武陽一、水牧仁一朗、工藤和恵、福水健次
- Organizer
  放射光学会
- Data Source
  KAKENHI-PUBLICLY-20H04648
[Presentation] 学習済み深層ニューラルネットワークからの解釈可能な物理法則抽出2020
- Author(s)
  本武陽一
- Organizer
  統計物理と統計科学のセミナー
- Data Source
  KAKENHI-PUBLICLY-20H04648

1. 竹内努 (90436072)

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2. 矢野恵佑 (20806070)

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3. 今泉允聡 (90814088)

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4. 佐々木博昭 (80756916)

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5. 金森敬文 (60334546)

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6. 末谷大道 (40507167)

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7. 高見利也 (10270472)

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8. 赤穗昭太郎 (40356340)

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9. 奥野彰文

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Mototake Yoh-ichi 本武 陽一

Research Projects

Research Products

Co-Researchers

位相的データ解析による階層構造をもつ大規模群れ運動の縮約モデリングとその応用Principal Investigator

Principal Investigator

Project Period (FY)

Research Category

Review Section

Research Institution

Machine-Learning-Reinforced Cosmic Structure Formation: From Large-Scale Structure Formation to Galaxy Evolution

Principal Investigator

Project Period (FY)

Research Category

Review Section

Research Institution

Constructing Induced self-organization in active matters using computational topology

Principal Investigator

Project Period (FY)

Research Category

Review Section

Research Institution

幾何学的データ解析手法の開発と位相的データ解析への展開

Principal Investigator

Project Period (FY)

Research Category

Review Section

Research Institution

Development of machine learning methods for discovering symmetries in pattern dynamicsPrincipal Investigator

Principal Investigator

Project Period (FY)

Research Category

Review Section

Research Institution

Constructing a reduced model of a pattern formation process on the basis of topological data analysisPrincipal Investigator

Principal Investigator

Project Period (FY)

Research Category

Review Section

Research Institution

[Journal Article] Autoregressive With Slack Time Series Model for Forecasting a Partially-Observed Dynamical Time Series2024

Author(s)

Journal Title

DOI

Data Source

[Journal Article] Quantifying physical insights cooperatively with exhaustive search for Bayesian spectroscopy of X-ray photoelectron spectra2023

Author(s)

Journal Title

DOI

Data Source

[Journal Article] Revealing the Mechanism of Large-scale Gradient Systems Using a Neural Reduced Potential2023

Author(s)

Journal Title

Data Source

[Journal Article] Extracting Nonlinear Symmetries From Trained Neural Networks on Dynamics Data2023

Author(s)

Journal Title

Data Source

[Journal Article] 位相的データ解析による銀河分布の定量化とバリオン音響振動抽出2023

Author(s)

Journal Title

Data Source

[Journal Article] Quantitative prediction of fracture toughness (<i>K</i><sub>I<i>c</i></sub>) of polymer by fractography using deep neural networks2022

Author(s)

Journal Title

DOI

Data Source

[Journal Article] Quantitative Prediction of Fracture Toughness (KIc) of Polymer by Fractography Using Deep Neural Networks2022

Author(s)

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Data Source

[Journal Article] Revealing the Mechanism of Magnetic Domain Formation by Topological Data Analysis2022

Author(s)

Journal Title

Data Source

[Journal Article] Revealing the Mechanism of Magnetic Domain Formation by Topological Data Analysis2022

Author(s)

Journal Title

Data Source

[Journal Article] Interpretable conservation law estimation by deriving the symmetries of dynamics from trained deep neural networks2021

Mototake Yoh-ichi 本武陽一