Data Driven Science Group

原著論文(査読付き) & 国際学会(査読付き)

1. H. IZUNO, M. Demura, M. Yamazaki, S. Minamoto, J. Sakurai, K. Nagata, Y. Mototake, D. Abe, K. Torigata, "Search for high-creep-strength welding conditions considering HAZ shape factors for 2 1/4Cr-1Mo steel," Welding in the World, (in press).
2. A. Okuno, Y. Morishita and Y. Mototake, "Autoregressive With Slack Time Series Model for Forecasting a Partially-Observed Dynamical Time Series," in IEEE Access, vol. 12, 24621-24630, 2024.
3. Yoh-ichi Mototake, "Extracting Nonlinear Symmetries From Trained Neural Networks on Dynamics Data," NeurIPS2023 Workshop on AI for Sciences: from Theory to Practice, 2023.
4. Shunya Tsuji, Ryo Murakami, Hayaru Shouno, Yoh-ichi Mototake, "Revealing the Mechanism of Large-scale Gradient Systems Using a Neural Reduced Potential," NeurIPS2023 Workshop on Machine Learning and the Physical Sciences(ML4PS) 2023.
5. Tsutomu T. Takeuchi, Kai T. Kono, Suchetha Cooray, Atsushi J. Nishizawa, Koya Murakami, Hai-Xia Ma, Yoh-Ichi Mototake, "Quantification of Galaxy Distribution with Topological Data Analysis and Detection of the Baryon Acoustic Oscillation," Proceedings of the Institute of Statistical Mathematics 71(2), 159-187, 2023.
6. Hiroyuki Kumazoe, Kazunori Iwamitsu, Masaki Imamura, Kazutoshi Takahashi, Yoh-ichi Mototake, Masato Okada, Ichiro Akai, "Quantifying physical insights cooperatively with exhaustive search for Bayesian spectroscopy of X-ray photoelectron spectra," Scientific Reports 13, 13221, 2023.
7. Yoh-ichi Mototake, Kaita Ito, Masahiko Demura, “Quantitative Prediction of Fracture Toughness of Polymer by Fractography Using Deep Neural Networks”, Science and Technology of Advanced Materials: Methods,vol. 2, 2022.
8. Junya Sakurai, Masahiko Demura,Yoh-ichi Mototake, Masato Okada, Masayoshi Yamazaki, Junya Inoue, "Descriptor extraction on inherent creep strength of carbon steel by exhaustive search", Science and Technology of Advanced Materials: Methods, vol. 1, 2021.
9. Yoh-ichi Mototake, Sadato Yamanaka, Takeshi Aoyagi, Takaaki Ohnishi, Kenji Fukumizu, "Free Energy Estimation of Metastable Structures of Block Copolymers using Topological Data Analysis ", Journal of Computer Chemistry Japan, 19(4), pp. 169 - 171, 2021.
10. Yoh-ichi Mototake, "Interpretable Conservation Law Estimation by Deriving the Symmetries of Dynamics from Trained Deep Neural Networks", Physical Review E, 103, 033303, 2021.
11. Narihisa Matsumoto, Yoh‐ichi Mototake, Kenji Kawano, Masato Okada & Yasuko Sugase‐Miyamoto, "Comparison of neuronal responses in primate inferior-temporal cortex and feed-forward deep neural network model with regard to information processing of faces", Journal of Computational Neuroscience, 2021.
12. Yoh-ichi Mototake, Sadato Yamanaka, Takeshi Aoyagi, Takaaki Ohnishi, Kenji Fukumizu, "Topological Data Analysis for microdomain patternsof Block Copolymer," Proceedings of the 2020 International Symposium on Nonlinear Theory and its Applications (NOLTA2020), 2020.
13. Kotaro Sakamoto, Yuichiro Mori, Yoh-ichi Mototake, "Towards a Geometrical Understanding of Physical Phenomena via Extraction of Data Manifolds using Generative Models", Proceedings of the 2020 International Symposium on Nonlinear Theory and its Applications (NOLTA2020), 2020.
14. Takayuki Niizato, Kotaro Sakamoto, Yoh-ichi Mototake, Hisashi Murakami, Yuta Nishiyama & Toshiki Fukushima, "Revealing the existence of the ontological commitment in fish schools", Artif Life Robotics 25, 633–642, 2020.
15. Takayuki Niizato, Kotaro Sakamoto, Yoh-ichi Mototake, Hisashi Murakami, Yuta Nishiyama, and Toshiki Fukushima, "Four-types of IIT-induced group integrity of Plecoglossus altivelis", Entropy, 22(7), 726, 2020.
16. Yoh-ichi Mototake, Hitoshi Izuno, Kenji Nagata, Masahiko Demura, Masato Okada, "A universal Bayesian inference framework for complicated creep constitutive equations", Scientific Reports, 6, 10437, 2020.
17. Hiroshi Shinotsuka, Hideki Yoshikawa, Yoh-ichi Mototake, Hayaru Shouno, Masato Okada, Kenji Nagata, "Development of spectral decomposition based on Bayesian information criterion with estimation of confidence interval", Science and Technology of Advanced Materials, Science and Technology of Advanced Materials, 6, 402-419, 2020.
18. Hitoshi Izuno, Masahiko Demura, Masaaki Tabuchi, Yoh-ichi Mototake, Masato Okada, "Data-based selection of creep constitutive models for high-Cr heat-resistant steel", Science and Technology of Advanced Materials, 21(1), 219-228, 2020.
19. Takayuki Niizato, Kotaro Sakamoto, Yoh-ichi Mototake, Hisashi Murakami, Takenori Tomaru, Tomotaro Hoshika, Toshiki Fukushima, "Finding continuity and discontinuity in fish schools via integrated information theory", PLoS ONE 15(2): e0229573, 2020.
20. Takayuki Niizato, Kotaro Sakamoto, Yoh-Ichi Mototake, Hisashi Murakami, Yuta Nishiyama, Toshiki Fukushima, "Heap Paradox in Fish Schools," SWARM2019 2019.
21. Yoh-ichi Mototake, "Conservation Law Estimation by Extracting the Symmetry of a Dynamical System Using a DNN," NeurIPS2019 Workshop on Machine Learning and the Physical Sciences(ML4PS) 2019.
22. Kenji Fukumizu, Shoichiro Yamaguch, Yoh-ichi Mototake, Mirai Tanaka, "Semi-flat minima and saddle points by embedding neural networks to overparameterization," Advances in Neural Information Processing Systems (NeurIPS) 2019.
23. Yoh-ichi Mototake, Hitoshi Izuno, Kenji Nagata, Masahiko Demura, Masato Okada, "Universal Framework of Bayesian Creep Model Selection for Steel," Materials Research Meeting 2019 December 10-14, 2019, Yokohama, Japan.
24. Yoh-ichi Mototake, Hitoshi Izuno, Kenji Nagata, Masahiko Demura, Masato Okada, "Universal Framework of Bayesian Creep Model Selection for Steel,"" nternational Conference on Computational & Experimental Engineering and Sciences , ICCES 2019 Volume 22 No 2,pp.129-130,2019.
25. Junya Sakurai, Junya Inoue, Masahiko Demura, Yoichi Mototake, Masato Okada, Masayoshi Yamazaki, "Descriptor Extraction on Inherent Creep Strength of Carbon Steels by Exhaustive Search," International Conference on Computational & Experimental Engineering and Sciences , ICCES 2019 Volume 22 No 2,pp.128-128,2019.
26. Hitoshi Izuno, Masahiko Demura, Masaaki Tabuchi, Yohichi Mototake, Masato Okada. "Creep Model Selection for Grade 91 Steel Using Data Scientific Method," International Conference on Computational & Experimental Engineering and Sciences , ICCES 2019 Volume 22 No 2,pp.121-121,2019.
27. 松平京介, 永田賢二, 本武陽一 and 岡田真人.“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”, 研究報告数理モデル化と問題解決（MPS）,2019-MPS-122(2),1-6, 2019.
28. Kenji Nagata, Yoh-ichi Mototake, Rei Muraoka, Takehiko Sasaki, and Masato Okada, “Bayesian Spectral Deconvolution Based on Poisson Distribution: Bayesian Measurement and Virtual Measurement Analytics (VMA)”, Journal of the Physical Society of Japan, 88(4), 044003, 2019.
29. Yoh-ichi Mototake, Masaichiro Mizumaki, Ichiro Akai, Masato Okada, “Bayesian Hamiltonian Selection in X-ray Photoelectron Spectroscopy”, Journal of the Physical Society of Japan, 88(3), 034004, 2019.
30. Yoh-ichi Mototake, Yasuhiko Igarashi, Hikaru Takenaka, Kenji Nagata, Masato Okada, “Spectral deconvolution through bayesian LARS-OLS”, Journal of the Physical Society of Japan, 87(11), 114004, 2018.
31. Takashi Ikegami, Yoh-ichi Mototake, Shintaro Kobori, Mizuki Oka, Yasuhiro Hashimoto, “Life as an emergent phenomenon: Studies from a large-scale boid simulation and web data”, Philosophical Transactions of The Royal Society A Mathematical Physical and Engineering Sciences, 375, 20160351, 2109, 2017.
32. Norihiro Maruyama, Yasuhiro Hashimoto, Yhoichi Mototake, Daichi Saito, Takashi Ikegami, "Revisiting Classification of Large Scale Flocking,"" The 2nd International Symposium on Swarm Behavior and Bio-Inspired Robotics, Oita, Japan; 10/2017.
33. 本武陽一, 福田玄明, and 植田一博. "人とエージェント間での内集団関係形成." 人工知能学会論文誌 31.6 (2016): AI30-J_1-10., DOI:10.1527/tjsai.AI30-J
34. Yhoichi Mototake, Takashi Ikegami "A Simulation Study of Large Scale Swarms," SWARM 2015, Kyoto, Japan; 10/2015.
35. Yhoichi Mototake, Takashi Ikegami "The dynamics of deep neural networks," the Twentieth International Symposium on Artificial Life and Robotics, Oita, Japan; 01/2015.

解説記事・書籍等

1. 本武陽一, 位相的データ解析法によるパターンダイナミクス分析のすすめ, 公益財団法人 統計情報研 究開発センター 機関誌「ESTRELA」10月号, (2021)
2. 書籍：デジタル化時代のAdditive Manufacturingの基礎と応用 リブロ社, (2021)
3. 本武陽一, 水牧仁一朗, 工藤和恵, 福水健次，位相的データ分析法による材料構造形成過程の分析 スマートプロセス学会誌，10(3) , (2021)
4. 本武陽一，庄野逸，田村弘，岡田真人，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- 情報処理，59(1), pp.42-47, (2017)

招待講演

1. Global Plasma Forum in Aomori 2023 （2023/10 Aomori）
2. ICIAM Tokyo 2023 Minisymposium "Perspectives in Artificial Intelligence and Machine Learning in Materials Chemistry" （2023/8 早稲田大学）
3. 第38回情報計測オンラインセミナー （2023/5 オンライン）
4. CRESTさきがけクラスター会議 （2022/10 京都大学）
5. 機械学会関東支部茨城ブロック　なるほど技術者講演会 （2022/7 茨城）
6. CRESTさきがけクラスター会議 （2022/2 オンライン）
7. 第69回化合物新磁性材料専門研究会 （2022/2 オンライン）
8. 第3回ORセミナー （2022/1 オンライン）
9. Material research meeting 2021 （2021/12 横浜）
10. ディープラーニングと物理学2020 （2020/7 オンライン）
11. 第2回日米独先端科学(JAGFoS)シンポジウム （2019/9 京都）
12. The 4th Workshop on Self-Organization and Robustness of Evolving Many-Body Systems （2019/3 東京)
13. 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 （2018/2/24 東京工芸大学厚木キャンパス）
14. 2017年日本金属学会秋期講演大会MIセッション （2017/9/6 北海道大学）（共同講演者）
15. 神経回路学会時限研究会「ニューラルネットの温故知新」 （2016/9/26, 27，電気通信大学）
16. 第11回全脳アーキテクチャ勉強会 （2015/8/26 リクルートGINZA8ビル）