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- MOTOTAKE Yoh-ichi
- Graduate School of Social Data Science
- MOTOTAKE Yoh-ichi
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Books and Other Publications
| 1. | デジタル化時代のadditive manufacturingの基礎と応用
中野, 貴由, 桐原, 聡秀, 近藤, 勝義, 西川, 宏, 田中, 学, スマートプロセス学会 リブロ社 2022.1 (ISBN : 9784915697371) |
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Papers
| 1. | Unsupervised Feature Selection Using Bayesian Tucker Decomposition (Peer-reviewed) Y-h. Taguchi, Yoh-ichi Mototake Neural Computation Vol.38,No.9,pp.1633-1657 2026.8 
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| 2. | Neural Reduced Potential via Persistent Homology (Peer-reviewed) Yoh-ichi Mototake 39th Conference on Neural Information Processing Systems (NeurIPS 2025) Workshop: Machine Learning and the Physical Sciences 2025.12 |
| 3. | Zero-dimensional modeling of drift wave turbulence by using Bayesian regression (Peer-reviewed) M Sasaki, Y Mototake, T Kobayashi, F Kin, G Yatomi, Y Kawachi Plasma Physics and Controlled Fusion Vol.67,No.9,pp.095011 2025.9
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| 4. | Natural language processing-based topic models for analyzing trends in polymer science (Peer-reviewed) Yoshifumi Amamoto, Yoh-ichi Mototake, Takaaki Ohnishi Polymer Journal 2025.5
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| 5. | Measurement of x-ray bremsstrahlung radiation from high energy electrons by stochastic acceleration in Heliotron J (Peer-reviewed) R. Yamato, S. Kobayashi, T. Fujita, K. Nagaoka, K. Nagasaki, S. Inagaki, T. Kawate, H. Ohgaki, T. Kii, H. Zen, S. Kado, T. Minami, H. Okada, S. Ohshima, S. Konoshima, T. Mizuuchi, Y. Mototake Review of Scientific Instruments Vol.96,No.3 2025.3
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| 6. | Procedure to reveal the mechanism of pattern formation process by topological data analysis (Peer-reviewed) Yoh-ichi Mototake, Masaichiro Mizumaki, Kazue Kudo, Kenji Fukumizu Physica D: Nonlinear Phenomena Vol.470,pp.134359 2024.12
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| 7. | Topological data analysis of large swarming dynamics (Peer-reviewed) Yoichi Mototake, Shinichi Ishida, Norihiro Maruyama, Takashi Ikegami NeurIPS2024 Workshop on Machine Learning and the Physical Sciences(ML4PS) 2024.12 |
| 8. | Information structure of heterogeneous criticality in a fish school. (Peer-reviewed) Takayuki Niizato, Kotaro Sakamoto, Yoh-Ichi Mototake, Hisashi Murakami, Takenori Tomaru Scientific reports Vol.14,No.1,pp.29758 2024.11
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| 9. | Autoregressive with Slack Time Series Model for Forecasting a Partially-Observed Dynamical Time Series (Peer-reviewed) A. Okuno, Y. Morishita, Y. Mototake IEEE Access 2024.2
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| 10. | Search for high-creep-strength welding conditions considering HAZ shape factors for 2 1/4Cr-1Mo steel (Peer-reviewed) Hitoshi IZUNO, Masahiko Demura, Masayoshi Yamazaki, Satoshi Minamoto, Junya Sakurai, Kenji Nagata, Yoh-ichi Mototake, Daisuke Abe, Keisuke Torigata Welding in the World 2024.2 |
| 11. | Revealing the Mechanism of Large-scale Gradient Systems Using a Neural Reduced Potential (Peer-reviewed) Shunya Tsuji, Ryo Murakami, Hayaru Shouno, Yoh-ichi Mototake NeurIPS 2023 Workshop: Machine Learning and the Physical Sciences 2023.12 |
| 12. | Extracting Nonlinear Symmetries From Trained Neural Networks on Dynamics Data (Peer-reviewed) Yoh-ichi Mototake NeurIPS 2023 Workshop: AI for Science from Theory to Practice 2023.12 |
| 13. | Quantification of Galaxy Distribution with Topological Data Analysis and Detection of the Baryon Acoustic Oscillation (Peer-reviewed) Tsutomu T. Takeuchi, Kai T. Kono, Suchetha Cooray, Atsushi J. Nishizawa, Koya Murakami, Hai-Xia Ma, Yoh-Ichi Mototake Proceedings of the Institute of Statistical Mathematics Vol.71,No.2,pp.159-187 2023.12 |
| 14. | Quantifying physical insights cooperatively with exhaustive search for Bayesian spectroscopy of X-ray photoelectron spectra (Peer-reviewed) Hiroyuki Kumazoe, Kazunori Iwamitsu, Masaki Imamura, Kazutoshi Takahashi, Yoh-ichi Mototake, Masato Okada, Ichiro Akai Scientific Reports Vol.13,No.1 2023.8
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| 15. | Quantitative prediction of fracture toughness (<i>K</i><sub>I<i>c</i></sub>) of polymer by fractography using deep neural networks (Peer-reviewed) Y. Mototake, K. Ito, M. Demura Science and Technology of Advanced Materials: Methods Vol.2,No.1,pp.310-321 2022.9
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| 16. | Interpretable conservation law estimation by deriving the symmetries of dynamics from trained deep neural networks (Peer-reviewed) Yoh-ichi Mototake Physical Review E Vol.103,pp.033303- 2021.3 |
| 17. | Comparison of neuronal responses in primate inferior-temporal cortex and feed-forward deep neural network model with regard to information processing of faces (Peer-reviewed) Narihisa Matsumoto, Yoh-ichi Mototake, Kenji Kawano, Masato Okada, Yasuko Sugase-Miyamoto Journal of Computational Neuroscience 2021.2
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| 18. | Free Energy Estimation of Metastable Structures of Block Copolymers using Topological Data Analysis (Peer-reviewed) Yoh-ichi Mototake, Sadato Yamanaka, Takeshi Aoyagi, Takaaki Ohnishi, Kenji Fukumizu Journal of Computer Chemistry Japan Vol.19,No.4,pp.169-171 2021 |
| 19. | Topological Data Analysis of Domain Pattern Formation in Materials (Peer-reviewed) 本武陽一, 水牧仁一朗, 工藤和恵, 福水健次 スマートプロセス学会誌 Vol.10,No.3,pp.108-119 2021
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| 20. | Development of spectral decomposition based on Bayesian information criterion with estimation of confidence interval (Peer-reviewed) Hiroshi Shinotsuka, Hideki Yoshikawa, Yoh-ichi Mototake, Hayaru Shouno, Masato Okada, Kenji Nagata Science and Technology of Advanced Materials 2020.6 |
| 21. | A universal Bayesian inference framework for complicated creep constitutive equations (Peer-reviewed) Yoh-ichi Mototake, Hitoshi Izuno, Kenji Nagata, Masahiko Demura, Masato Okada Scientific Reports 2020.6 |
| 22. | Topological Data Analysis for microdomain patternsof Block Copolymer (Peer-reviewed) Yoh-ichi Mototake, Sadato Yamanaka, Takeshi Aoyagi, Takaaki Ohnishi, Kenji Fukumizu Proceedings of the 2020 International Symposium on Nonlinear Theory and its Applications 2020 |
| 23. | Revealing the existence of the ontological commitment in fish schools (Peer-reviewed) Takayuki Niizato, Kotaro Sakamoto, Yoh-ichi Mototake, Hisashi Murakami, Takenori Tomaru, Tomotaro Hoshika, Toshiki Fukushima Artif Life Robotics Vol.25,pp.633-633–642 2020
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| 24. | Towards a Geometrical Understanding of Physical Phenomena via Extraction of Data Manifolds using Generative Models (Peer-reviewed) Kotaro Sakamoto, Yuichiro Mori, Yoh-ichi Mototake Proceedings of the 2020 International Symposium on Nonlinear Theory and its Applications 2020 |
| 25. | Finding continuity and discontinuity in fish schools via integrated information theory. (Peer-reviewed) Takayuki Niizato, Kotaro Sakamoto, Yoh-Ichi Mototake, Hisashi Murakami, Takenori Tomaru, Tomotaro Hoshika, Toshiki Fukushima PloS one Vol.15,No.2,pp.e0229573- 2020
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| 26. | Four-types of IIT-induced group integrity of Plecoglossus altivelis (Peer-reviewed) Takayuki Niizato, Kotaro Sakamoto, Yoh-ichi Mototake, Hisashi Murakami, Yuta Nishiyama, Toshiki Fukushima Entropy Vol.22,No.7,pp.726 2020
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| 27. | Data-based selection of creep constitutive models for high-Cr heat-resistant steel (Peer-reviewed) IZUNO, Hitoshi, Demura, Masahiko, TABUCHI, Masaaki, Mototake, Yoh-ichi, Okada, Masato Science and Technology of Advanced Materials Vol.21,No.1,pp.219-228 2020 |
| 28. | Universal Framework of Bayesian Creep Model Selection for Steel, (Peer-reviewed) Yoh-ichi Mototake, Hitoshi Izuno, Kenji Nagata, Masahiko Demura, Masato Okada Materials Research Meeting 2019 2019.12 |
| 29. | Universal Framework of Bayesian Creep Model Selection for Steel, (Peer-reviewed) Yoh-ichi Mototake, Hitoshi Izuno, Kenji Nagata, Masahiko Demura, Masato Okada International Conference on Computational & Experimental Engineering and Sciences 2019.3 |
| 30. | Descriptor Extraction on Inherent Creep Strength of Carbon Steels by Exhaustive Search (Peer-reviewed) Junya Sakurai, Junya Inoue, Masahiko Demura, Yoichi Mototake, Masato Okada, Masayoshi Yamazaki International Conference on Computational & Experimental Engineering and Sciences 2019 |
| 31. | Conservation Law Estimation by Extracting the Symmetry of a Dynamical System Using a DNN (Peer-reviewed) Yoh-ichi Mototake NeurIPS2019 Workshop on Machine Learning and the Physical Sciences(ML4PS) 2019 |
| 32. | Semi-flat minima and saddle points by embedding neural networks to overparameterization (Peer-reviewed) Kenji Fukumizu, Shoichiro Yamaguch, Yoh-ichi Mototake, Mirai Tanaka Advances in Neural Information Processing Systems (NeurIPS) 2019 |
| 33. | Bayesian Hamiltonian Selection in X-ray Photoelectron Spectroscopy (Peer-reviewed) Yoh-ichi Mototake, Masaichiro Mizumaki, Ichiro Akai, Masato Okada Journal of the Physical Society of Japan Vol.88,No.3 2019
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| 34. | Bayesian Spectral Deconvolution Based on Poisson Distribution: Bayesian Measurement and Virtual Measurement Analytics (VMA) (Peer-reviewed) Kenji Nagata, Yoh-ichi Mototake, Rei Muraoka, Takehiko Sasaki, Masato Okada Journal of the Physical Society of Japan Vol.88,No.4,pp.044003 2019
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| 35. | Creep Model Selection for Grade 91 Steel Using Data Scientific Method (Peer-reviewed) Hitoshi Izuno, Masahiko Demura, Masaaki Tabuchi, Yohichi Mototake, Masato Okada International Conference on Computational & Experimental Engineering and Sciences 2019 |
| 36. | レプリカ交換モンテカルロ法を用いたMixture of Experts モデルにおけるベイズ推論 (Peer-reviewed) 松平京介, 永田賢二, 本武陽一, 岡田真人 情報処理学会論文誌数理モデル化と応用 (TOM) 2019 |
| 37. | Heap Paradox in Fish Schools (Peer-reviewed) Takayuki Niizato, Kotaro Sakamoto, Yoh-Ichi Mototake, Hisashi Murakami, Yuta Nishiyama, Toshiki Fukushima SWARM2019 2019 |
| 38. | Spectral deconvolution through bayesian LARS-OLS (Peer-reviewed) Yoh-ichi Mototake, Yasuhiko Igarashi, Hikaru Takenaka, Kenji Nagata, Masato Okada Journal of the Physical Society of Japan Vol.87,No.11 2018 |
| 39. | Life as an emergent phenomenon: studies from a large-scale boid simulation and web data (Peer-reviewed) Takashi Ikegami, Yoh-ichi Mototake, Shintaro Kobori, Mizuki Oka, Yasuhiro Hashimoto PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES Vol.375,No.2109 2017.12
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| 40. | Revisiting Classification of Large Scale Flocking (Peer-reviewed) Norihiro Maruyama, Yasuhiro Hashimoto, Yhoichi Mototake, Daichi Saito, Takashi Ikegami The 2nd International Symposium on Swarm Behavior and Bio-Inspired Robotics 2017 |
| 41. | Creating an in-group relation between humans and agents (Peer-reviewed) Yoh Ichi Mototake, Haruaki Fukuda, Kazuhiro Ueda Transactions of the Japanese Society for Artificial Intelligence Vol.31,No.6,pp.AI30-J_1-10- 2016
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| 42. | The dynamics of deep neural networks (Peer-reviewed) Yhoichi Mototake, Takashi Ikegami the Twentieth International Symposium on Artificial Life and Robotics 2015 |
| 43. | A Simulation Study of Large Scale Swarms (Peer-reviewed) Yhoichi Mototake, Takashi Ikegami The 1st International Symposium on Swarm Behavior and Bio-Inspired Robotics 2015 2015 |
Misc.
| 1. | Integrating Bayesian Spectral Deconvolution and Expert Scientific Reasoning for Robust Peak Estimation
Hayato Okubo, Yoshifumi Amamoto, Toshimitsu Aritake, Hiroyuki Kumazoe, Shiryu Nakano, Evan Jamison, Satoshi Tanaka, Yoh-ichi Mototake 2026.5
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| 2. | Uncertainties in Physics-informed Inverse Problems: The Hidden Risk in Scientific AI
Yoh-ichi Mototake, Makoto Sasaki 2025.11
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| 3. | Algebraic Geometrical Analysis of Metropolis Algorithm When Parameters Are Non-identifiable
Kenji Nagata, Yoh-ichi Mototake 2024.6
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| 4. | Dynamics-Based Intrinsic Signal Model for High-Dimensional, Small-Sample Data
Yoh-ichi Mototake, Y-h. Taguchi 2023.4
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| 5. | Topological Data Analysis of Domain Pattern Formation in Materials (Peer-reviewed) 本武陽一, 水牧仁一朗, 工藤和恵, 福水健次 スマートプロセス学会誌 Vol.10,No.3,pp.108-119 2021
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| 6. | 脳情報科学が拓くAIとICT：2．脳情報科学と人工知能 -ネオコグニトロンからDeep Learningへ-
本武 陽一, 庄野逸, 田村弘, 岡田真人 情報処理 Vol.59,No.1,pp.42-47 2017 |
Presentations
| No. | Name of subject/Conference Name | Year | Site |
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| 1. | 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(ディープラーニングと物理学2020 オンライン) |
Holding date :
Presentation date : 2020.7.30 |
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| 2. | 第2回日米独先端科学(JAGFoS)シンポジウム(第2回日米独先端科学(JAGFoS)シンポジウム) |
Holding date :
Presentation date : 2019.10 |
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| 3. | The 4th Workshop on Self-Organization and Robustness of Evolving Many-Body Systems(The 4th Workshop on Self-Organization and Robustness of Evolving Many-Body Systems) |
Holding date :
Presentation date : 2019.3 |
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| 4. | 第2回教育・コミュニケーションロボット研究開発シンポジウム(第2回教育・コミュニケーションロボット研究開発シンポジウム) |
Holding date :
Presentation date : 2018 |
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| 5. | 神経回路学会時限研究会「ニューラルネットの温故知新」(神経回路学会時限研究会「ニューラルネットの温故知新」) |
Holding date :
Presentation date : 2016 |
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| 6. | 第11回全脳アーキテクチャ勉強会(第11回全脳アーキテクチャ勉強会) |
Holding date :
Presentation date : 2015 |
Awards
| No. | Award name | Year |
|---|---|---|
| 1. | International Conference on Machine Learning (ICML) Gold Reviewer | 2026.6 |
| 2. | The 3rd International Symposium on Swarm Behavior and Bio-Inspired Robotics, Best Paper Award Finalists | 2019 |
| 3. | 脳学際研究部門第1回公開シンポジウム 最優秀発表賞 | 2017 |
| 4. | 2016 Annual Conference Award | 2016 |
| 5. | The First International Symposium on Swarm Behavior and Bio-Inspired Robotics, Best Student Paper Award Finalists | 2015 |
Research Projects
| No. | Research subject | Research item(Awarding organization, System name) | Year |
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| 1. | Development and Application of Persistent Homology Analysis for Spatio-Temporal Pattern Data
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Grant-in-Aid for Scientific Research (B)
( Awarding organization: Japan Society for the Promotion of Science System name: Grants-in-Aid for Scientific Research ) |
2026.4 - 2031.3 |
| 2. | Comprehensive study of kinetic turbulence phenomena in the steep gradient region of magnetically confined plasmas
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Grant-in-Aid for Scientific Research (B)
( Awarding organization: Japan Society for the Promotion of Science System name: Grants-in-Aid for Scientific Research ) |
2025.4 - 2030.3 |
| 3. | 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
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学術変革領域研究(A)
( Awarding organization: 日本学術振興会 System name: 科学研究費助成事業 ) |
2025.4 - 2027.3 |
| 4. | 極限環境対応構造材料研究拠点(RISME)
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( Awarding organization: 文部科学省 System name: データ創出・活用型マテリアル研究開発プロジェクト ) |
2025.4 |
| 5. | Constructing Induced self-organization in active matters using computational topology
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Grant-in-Aid for Challenging Research (Exploratory)
( Awarding organization: Japan Society for the Promotion of Science System name: Grants-in-Aid for Scientific Research ) |
2024.6 - 2026.3 |
| 6. | Machine-Learning-Reinforced Cosmic Structure Formation: From Large-Scale Structure Formation to Galaxy Evolution
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Grant-in-Aid for Scientific Research (A)
( Awarding organization: Japan Society for the Promotion of Science System name: Grants-in-Aid for Scientific Research ) |
2024.4 - 2029.3 |
| 7. | マルチモーダル計測に基づく光機能デバイスのマルチスケールダイナミクス解析
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( Awarding organization: 科学技術振興機構 System name: 戦略的な研究開発の推進 戦略的創造研究推進事業 CREST ) |
2024 - 2029 |
| 8. | 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
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基盤研究(B)
( Awarding organization: 日本学術振興会 System name: 科学研究費助成事業 ) |
2023.4 - 2028.3 |
| 9. | 革新的セラミック材料設計のための材料パターン情報学の創成
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( Awarding organization: 国立研究開発法人 新エネルギー・産業技術総合開発機構（NEDO） System name: 未踏チャレンジ２０５０ ) |
2022.8 - 2025.7 |
| 10. | Development of machine learning methods for discovering symmetries in pattern dynamics
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Grant-in-Aid for Early-Career Scientists
( Awarding organization: Japan Society for the Promotion of Science System name: Grants-in-Aid for Scientific Research Grant-in-Aid for Early-Career Scientists ) |
2022.4 - 2027.3 |
| 11. | 解釈可能AIによるパターンダイナミクスの数理構造抽出と材料情報学への応用
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さきがけ
( Awarding organization: 国立研究開発法人科学技術振興機構 System name: 戦略的創造研究推進事業（さきがけ） ) |
2021.10 - 2025.3 |
| 12. | Constructing a reduced model of a pattern formation process on the basis of topological data analysis
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Grant-in-Aid for Scientific Research on Innovative Areas (Research in a proposed research area)
( Awarding organization: Japan Society for the Promotion of Science System name: Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research on Innovative Areas (Research in a proposed research area) ) |
2020.4 - 2022.3 |
| 13. | 代数幾何的学習理論の物理データ分析への応用手法の検討
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一般研究２
( Awarding organization: 統計数理研究所 System name: 統計数理研究所共同利用 ) |
2020.4 - 2021.3 |
| 14. | TDAによる強磁性体磁区パターン形成過程の分析
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一般研究２
( Awarding organization: 統計数理研究所 System name: 統計数理研究所共同利用 ) |
2020.4 - 2021.3 |
