Details of the Researcher

PHOTO

Yoichi Takagishi
Section
New Industry Creation Hatchery Center
Job title
Specially Appointed Associate Professor(Research)
e-Rad No.
00995136

Research Areas 1

  • Manufacturing technology (mechanical, electrical/electronic, chemical engineering) / Chemical reaction and process system engineering / Multiphysics simulation, Machine Learning, Process informatics, Li-ion battery, Catalyst, Crystal Growth

Papers 37

  1. Latent-Feature-Based Classification of Macroscopic Surface Morphologies and Parameter Optimization in Electron Beam Additive Manufacturing of Ti-6Al-4V Peer-reviewed

    Yoichi Takagishi, Yusaku Sato, Shoichi Sato, Ikeshoji Toshi-Taka, Akihiko Chiba

    Materials Today Communications　2026

    DOI： 10.1016/j.mtcomm.2026.116089 　

  2. Melting and Solidification Behaviors of Titanium Nano-Particles: Comparison between Two Molecular Dynamics Models

    Yoichi Takagishi, Toshi-Taka Ikeshoji, Akihiko Chiba

    MATERIALS TRANSACTIONS　MT-M2026080　2026

    Publisher: Japan Institute of Metals

    DOI： 10.2320/matertrans.mt-m2026080 　

    ISSN： 1345-9678

    eISSN： 1347-5320

  3. Capabilities of Large Language Models in Interpreting Melting and Solidification Dynamics of Metal Powders from Molecular Dynamics Data

    Yoichi Takagishi, Akihiko Chiba

    2026

    DOI： 10.2139/ssrn.7397380 　

  4. Autonomous Selection of Li-ion Battery Degradation Models Using Capacity Retention Data via Parallel Scenario Computing Peer-reviewed

    Yoichi Takagishi, Tatsuya Yamaue

    International Journal of Automotive Engineering　17　(3)　119-126　2026

    Publisher: Society of Automotive Engineers of Japan, Inc.

    DOI： 10.20485/jsaeijae.17.3_119 　

    eISSN： 2185-0992

  5. Modelling of Thermal Runaway Propagation in Li-Ion Battery Cells Considering Variations in Thermal Property Measurements Peer-reviewed

    Hayato Kitagawa, Yoichi Takagishi, Masato Nishiuchi, Koichi Saeki, Ryohei Baba, Tatsuya Yamaue

    Batteries　11　(10)　386-386　2025/10/21

    Publisher: MDPI AG

    DOI： 10.3390/batteries11100386 　

    eISSN： 2313-0105

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    Physics-based simulations of lithium-ion battery thermal runaway (TR) and thermal propagation (TP) enable the assessment of diverse temperature behaviors among individual cells. These behaviors are primarily driven by variations in thermal properties and the amount of heat released during thermal decomposition. However, given the inherent variability in thermal property measurements, the specific values adopted can lead to substantial differences in predicted temperature behavior. In this study, we developed a 1-dimensional TP model for an array of three prismatic lithium-ion battery cells, in consideration of the uncertainty of key thermal parameters including specific heat, thermal conductivity, activation energy, and the latent heat of the thermal decomposition reaction. The validity of the model and the identification of calibration parameters are ensured through comparison with experimentally measured temperatures. We evaluated the influence of these parameter variations on the temperature and thermal runaway behavior of each cell. Our findings indicate that the variation in thermal runaway timing increases with distance from the trigger cell, and the probability of thermal runaway in the end cell was significantly higher than in the center cell. A sensitivity analysis using a surrogate model revealed that cell temperature is more sensitive to variations in thermal conductivity and latent heat than to variations in specific heat and activation energy.

  6. Analytical Solution for Thermal Runaway of Li-Ion Battery with Simplified Thermal Decomposition Equation Peer-reviewed

    Yoichi Takagishi, Hayato Kitagawa, Tatsuya Yamaue

    Applied Sciences　15　(12)　6574-6574　2025/06/11

    Publisher: MDPI AG

    DOI： 10.3390/app15126574 　

    eISSN： 2076-3417

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    Analytical solutions for the temperature change of a lithium-ion battery during thermal runaway were derived by the equation of linearizing thermal decomposition reaction. This study focuses on the representative temperature of the battery cell (zero-dimensional) during the heating test. First, the thermal decomposition reaction was modeled from DSC tests data of the electrode assuming Arrhenius-type temperature dependency. Subsequently, the reaction was simplified by a linear function of temperature and the analytical solution was derived as the exponential function with respect to time. The validity and applicability of the analytical solution are discussed by comparing it with a one-dimensional thermal runaway simulation. Further study was carried out for multiple batteries in consideration of cell-to-cell propagation of the thermal runaway and the applicability was discussed. As results, the single-cell predictions agreed generally with numerical results, especially with higher heating and lower latent heat. A delay in thermal runaway onset in multiple cells, linearly dependent on inter-cell conductivity, was quantified analytically. Parameter adjustments improved the alignment of analytical and numerical results for multiple cells, enabling quick thermal assessments. While numerical simulation is needed for high accuracy, this analytical framework offers new insights and facilitates initial analyses.

  7. Analysis of Artificial Microstructure of Li-ion Battery Electrode Using Persistent Homology and Machine Learning Peer-reviewed

    Kanji MIYAZAKI, Yoichi TAKAGISHI, Tatsuya YAMAUE

    Electrochemistry　2025

    DOI： 10.5796/electrochemistry.25-00120 　

  8. Modelling of SiOx electrode degradation based on latent variables from 2D-SEM images Peer-reviewed

    Yoichi Takagishi, Yoshiki Hayashi, Takayuki Tsubota, Tatsuya Yamaue

    Journal of Energy Storage　105　114621-114633　2025/01

    Publisher: Elsevier BV

    DOI： 10.1016/j.est.2024.114621 　

    ISSN： 2352-152X

  9. Mesoscopic Modelling of Li Deposition in Sulfide-Based Solid Electrolyte Using Two-Dimensional Artificial Polycrystal Structure Peer-reviewed

    Yoichi Takagishi, Tatsuya Yamaue

    Journal of The Electrochemical Society　2024/12/03

    DOI： 10.1149/1945-7111/ad9a0c 　

  10. Multi-scale Simulations in a Catalytic Reactor Considering Three-dimensional Pellet Geometry Peer-reviewed

    Yoichi Takagishi, Ryohei BABA, Wataru ADACHI, Takeshi YAMASHITA

    Chemical Engineering Transactions　108　43-48　2024/03

    DOI： 10.3303/CET24108008 　

  11. Cavitation Damage Prediction in Mercury Target for Pulsed Spallation Neutron Source Using Monte Carlo Simulation Peer-reviewed

    Takashi Wakui, Yoichi Takagishi, Masatoshi Futakawa

    Materials　16　(17)　5830-5830　2023/08/25

    Publisher: MDPI AG

    DOI： 10.3390/ma16175830 　

    eISSN： 1996-1944

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    Cavitation damage on a mercury target vessel for a pulsed spallation neutron source is induced by a proton beam injection in mercury. Cavitation damage is one of factors affecting the allowable beam power and the life time of a mercury target vessel. The prediction method of the cavitation damage using Monte Carlo simulations was proposed taking into account the uncertainties of the core position of cavitation bubbles and impact pressure distributions. The distribution of impact pressure attributed to individual cavitation bubble collapsing was assumed to be Gaussian distribution and the probability distribution of the maximum value of impact pressures was assumed to be three kinds of distributions: the delta function and Gaussian and Weibull distributions. Two parameters in equations describing the distribution of impact pressure were estimated using Bayesian optimization by comparing the distribution of the cavitation damage obtained from the experiment with the distribution of the accumulated plastic strain obtained from the simulation. Regardless of the distribution type, the estimated maximum impact pressure was 1.2–2.9 GPa and existed in the range of values predicted by the ratio of the diameter and depth of the pit. The estimated dispersion of the impact pressure distribution was 1.0–1.7 μm and corresponded to the diameter of major pits. In the distribution of the pits described by the accumulated plastic strain, which was assumed in three cases, the delta function and Gaussian and Weibull distributions, the Weibull distribution agreed well with the experimental results, particularly including relatively large pit size. Furthermore, the Weibull distribution reproduced the depth profile, i.e., pit shape, better than that using the delta function or Gaussian distribution. It can be said that the cavitation erosion phenomenon is predictable by adopting the Weibull distribution. This prediction method is expected to be applied to predict the cavitation damage in fluid equipment such as pumps and fluid parts.

  12. モンテカルロ・シミュレーションによる核破砕パルス中性子源水銀標的容器におけるキャビテーション損傷予測 Peer-reviewed

    涌 井 隆, 高 岸 洋 一, 二 川 正 敏, 田 邉 誠

    実験力学　23　(2)　168-174　2023/06

    DOI： 10.11395/jjsem.23.168 　

  13. Mathematical Modeling of Multiple-Li-Dendrite Growth in Li-ion Battery Electrodes Peer-reviewed

    Yoichi Takagishi, Tatsuya Yamaue

    Journal of The Electrochemical Society　170　(3)　030528-030528　2023/03/01

    Publisher: The Electrochemical Society

    DOI： 10.1149/1945-7111/acc2eb 　

    ISSN： 0013-4651

    eISSN： 1945-7111

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    Lithium dendrite growth in Li-ion batteries is one of the most dangerous phenomena because it can cause inner short circuits and thermal runaways. However, the nucleation and growth of the dendrites are difficult to predict because of their complex behaviors, which depend on several factors such as the charging conditions and electrode-design parameters. In this study, a comprehensive mathematical model has been developed for Li-deposition on Li-ion battery electrodes. The model is based on the Single Particle model (to evaluate the Li-ion concentration fields) combined with dendrite-growth models based on the electrochemical and crystal growth theory. The effect of the SEI thickness distribution, the charging C-rate and cut-off voltage on the growth of dendrite tips have been statistically evaluated, and the risk of short circuit is discussed. The study focuses on the effects of the SEI-thickness distribution on the timing of the SEI breaking and density of the dendrite formation.

  14. Battery Degradation Modeling Based on FIB-SEM Image Features Extracted by Deep Neural Network

    Yoichi Takagishi, Tatsuya Yamaue

    KOBELCO TECHNOLOGY REVIEW　40　29-34　2023/01

  15. Heating simulation of a Li-ion battery cylindrical cell and module with consideration of gas ejection Peer-reviewed

    Yoichi Takagishi, Yasufumi Tozuka, Takumi Yamanaka, Tatsuya Yamaue

    Energy Reports　8　3176-3188　2022/11

    Publisher: Elsevier BV

    DOI： 10.1016/j.egyr.2022.02.086 　

    ISSN： 2352-4847

  16. Application of an Artificial Neural Network to Develop Fracture Toughness Predictor of Ferritic Steels Based on Tensile Test Results Peer-reviewed

    Kenichi Ishihara, Hayato Kitagawa, Yoichi Takagishi, Toshiyuki Meshii

    Metals　11　(11)　1740-1740　2021/10/30

    Publisher: MDPI AG

    DOI： 10.3390/met11111740 　

    eISSN： 2075-4701

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    Analyzing the structural integrity of ferritic steel structures subjected to large temperature variations requires the collection of the fracture toughness (KJc) of ferritic steels in the ductile-to-brittle transition region. Consequently, predicting KJc from minimal testing has been of interest for a long time. In this study, a Windows-ready KJc predictor based on tensile properties (specifically, yield stress σYSRT and tensile strength σBRT at room temperature (RT) and σYS at KJc prediction temperature) was developed by applying an artificial neural network (ANN) to 531 KJc data points. If the σYS temperature dependence can be adequately described using the Zerilli–Armstrong σYS master curve (MC), the necessary data for KJc prediction are reduced to σYSRT and σBRT. The developed KJc predictor successfully predicted KJc under arbitrary conditions. Compared with the existing ASTM E1921 KJc MC, the developed KJc predictor was especially effective in cases where σB/σYS of the material was larger than that of RPV steel.

  17. 回折環がスポッティな場合に対するcosα法方式X線応力測定 Peer-reviewed

    藤本 洋平, 中本 久志, 高岸 洋一, 佐々木 敏彦

    非破壊検査　70　(9)　406-412　2021/09

    DOI： 10.11396/jjsndi.70.406 　

  18. 機械学習・深層学習を用いたデータ駆動型バッテリー劣化予測技術

    高岸洋一, 山上達也

    R&D神戸製鋼技報　71　(1)　48-52　2021/07

  19. 人工ニューラルネットワークを用いたリチウムイオン電池電極の最適メソスケール構造探索

    山中拓己, 高岸洋一, 山上達也

    R&D神戸製鋼技報　71　(1)　41-47　2021/07

  20. Quasi-3D modeling of Li-ion batteries based on single 2D image Peer-reviewed

    Yoichi Takagishi, Takumi Yamanaka, Tatsuya Yamaue

    SN Applied Sciences　3　(6)　2021/05/17

    Publisher: Springer Science and Business Media LLC

    DOI： 10.1007/s42452-021-04581-w 　

    ISSN： 2523-3963

    eISSN： 2523-3971

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    Abstract Electrochemical physics-based simulations of Li-ion batteries using a mesoscale 3D structure of porous electrodes are one of the most effective approaches for evaluating the local Li concentration in active materials and the Li-ion concentration in electrolytes. However, this approach requires considerable computational resources compared with a simple 2D or 1D homogeneous simulation. In this work, we developed an advanced electrochemical physics-based simulation method for Li-ion batteries that enabled a quasi-3D simulation of charge/discharge using only a single 2D slice image. The governing equations were based on typical theories of electrochemical reactions and ion transport. From referencing the 2D plane, the model was able to simulate both the Li concentration in the active material and the Li-ion concentration in the electrolyte for their subsequent consideration in a virtual 3D structure. To confirm the validity of our proposed model, a full 3D discharge simulation with randomly packed active material particles was performed and compared with the results of the quasi-3D model and a simple-2D model. Results indicated that the quasi-3D model properly reproduced the sliced Li and Li-ion concentrations simulated by the full 3D model in the charge/discharge process, whereas the simple-2D simulation partially overestimated or underestimated these concentrations. In addition, the quasi-3D model made it possible to dramatically decrease the computation time compared to the full-3D model. Finally, we applied the model to an actual scanning electron microscopy equipped with a focused ion beam (FIB-SEM) image of a positive electrode. Graphic abstract

  21. An Electrochemical-Thermal Model for Lithium-Ion Battery Packs during Driving of Battery Electric Vehicles Peer-reviewed

    Takumi Yamanaka, Yoichi Takagishi, Tatsuya Yamaue

    Journal of The Electrochemical Society　168　(5)　050545-050545　2021/05/01

    Publisher: The Electrochemical Society

    DOI： 10.1149/1945-7111/ac01e2 　

    ISSN： 0013-4651

    eISSN： 1945-7111

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    In this study, an electrochemical–thermal coupled model is proposed to predict phenomena in battery packs that consist of lithium-ion battery cells during the driving of battery electric vehicles (BEVs). The model considers the cycle degradation and internal short circuits per cell and can quantitatively evaluate the temperature, loss capacity, and internal resistance per cell. Using this model, simulations are performed focusing on the three impacts of (i) the short-circuit object electric resistance, (ii) number of runs, and (iii) environmental temperature. When the short-circuit object resistance is 5 Ω, the temperature rise in the first run is 6.0 times higher and the loss capacity is 1.7 times higher than that in the non-shorted condition, and it is also confirmed that the risk of thermal runaway is high because the short-circuit object reaches a maximum of 114.2 °C. If there are no short-circuited cells, in repeated runs at an environmental temperature of 40 °C, the driving range at the 300th run is 17 % lower than that of the first run. The loss of the driving range is 3.5 times larger than that at 20 °C, which indicates that the cycle degradation progresses approximately 3.5 times faster.

  22. 機械学習を用いたリチウムイオン電池劣化モデリング

    高岸洋一, 山中拓己, 山上達也

    電気化学（Denki Kagaku）　89　(2)　140-144　2021

  23. Multi-Physics Equivalent Circuit Models for a Cooling System of a Lithium Ion Battery Pack Peer-reviewed

    Takumi Yamanaka, Daiki Kihara, Yoichi Takagishi, Tatsuya Yamaue

    Batteries　6　(3)　44-44　2020/08/29

    Publisher: MDPI AG

    DOI： 10.3390/batteries6030044 　

    eISSN： 2313-0105

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    Lithium (Li)-ion battery thermal management systems play an important role in electric vehicles because the performance and lifespan of the batteries are affected by the battery temperature. This study proposes a framework to establish equivalent circuit models (ECMs) that can reproduce the multi-physics phenomenon of Li-ion battery packs, which includes liquid cooling systems with a unified method. We also demonstrate its utility by establishing an ECM of the thermal management systems of the actual battery packs. Experiments simulating the liquid cooling of a battery pack are performed, and a three-dimensional (3D) model is established. The 3D model reproduces the heat generated by the battery and the heat transfer to the coolant. The results of the 3D model agree well with the experimental data. Further, the relationship between the flow rate and pressure drop or between the flow rate and heat transfer coefficients is predicted with the 3D model, and the data are used for the ECM, which is established using MATLAB Simulink. This investigation confirmed that the ECM’s accuracy is as high as the 3D model even though its computational costs are 96% lower than the 3D model.

  24. A Framework for Optimal Safety Li-ion Batteries Design using Physics-Based Models and Machine Learning Approaches Peer-reviewed

    Takumi Yamanaka, Yoichi Takagishi, Tatsuya Yamaue

    Journal of The Electrochemical Society　167　(10)　100516-100516　2020/06/05

    Publisher: The Electrochemical Society

    DOI： 10.1149/1945-7111/ab975c 　

    eISSN： 1945-7111

  25. Quantitative comparison of the predictions of fracture toughness temperature dependence using ASTM E1921 master curve and stress distribution T-scaling methods Peer-reviewed

    Toshiyuki Meshii, Goh Yakushi, Yoichi Takagishi, Yohei Fujimoto, Kenichi Ishihara

    Engineering Failure Analysis　111　104458-104458　2020/04

    Publisher: Elsevier BV

    DOI： 10.1016/j.engfailanal.2020.104458 　

    ISSN： 1350-6307

  26. Machine Learning Approaches for Designing Mesoscale Structure of Li-Ion Battery Electrodes Peer-reviewed

    Yoichi Takagishi, Takumi Yamanaka, Tatsuya Yamaue

    Batteries　5　(3)　54-54　2019/08/01

    Publisher: MDPI AG

    DOI： 10.3390/batteries5030054 　

    eISSN： 2313-0105

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    We have proposed a data-driven approach for designing the mesoscale porous structures of Li-ion battery electrodes, using three-dimensional virtual structures and machine learning techniques. Over 2000 artificial 3D structures, assuming a positive electrode composed of randomly packed spheres as the active material particles, are generated, and the charge/discharge specific resistance has been evaluated using a simplified physico-chemical model. The specific resistance from Li diffusion in the active material particles (diffusion resistance), the transfer specific resistance of Li+ in the electrolyte (electrolyte resistance), and the reaction resistance on the interface between the active material and electrolyte are simulated, based on the mass balance of Li, Ohm’s law, and the linearized Butler–Volmer equation, respectively. Using these simulation results, regression models, using an artificial neural network (ANN), have been created in order to predict the charge/discharge specific resistance from porous structure features. In this study, porosity, active material particle size and volume fraction, pressure in the compaction process, electrolyte conductivity, and binder/additives volume fraction are adopted, as features associated with controllable process parameters for manufacturing the battery electrode. As a result, the predicted electrode specific resistance by the ANN regression model is in good agreement with the simulated values. Furthermore, sensitivity analyses and an optimization of the process parameters have been carried out. Although the proposed approach is based only on the simulation results, it could serve as a reference for the determination of process parameters in battery electrode manufacturing.

  27. Modeling lithium ion battery nail penetration tests and quantitative evaluation of the degree of combustion risk Peer-reviewed

    Takumi Yamanaka, Yoichi Takagishi, Yasufumi Tozuka, Tatsuya Yamaue

    Journal of Power Sources　416　132-140　2019/03

    Publisher: Elsevier BV

    DOI： 10.1016/j.jpowsour.2019.01.055 　

    ISSN： 0378-7753

  28. リチウムイオン二次電池向けシミュレーション技術

    高岸洋一, 山上達也

    R&D神戸製鋼技報　66　(2)　120-125　2017/03

  29. Prediction of Li-ion Battery Module Performance under Running Condition Based on "Multifactorial Degradation Model" Peer-reviewed

    Yoichi Takagishi, Tatsuya Yamaue

    International Journal of Automotive Engineering　8　(3)　143-148　2017

    Publisher: Society of Automotive Engineers of Japan, Inc.

    DOI： 10.20485/jsaeijae.8.3_143 　

    ISSN： 2185-0984

    eISSN： 2185-0992

  30. 吊橋主ケーブルの実橋送気試験および送気シミュレーション解析

    峰地慎一, 橋田芳郎, 隠岐保博, 森西義章, 高岸洋一

    R&D神戸製鋼技報　65　(1)　74-79　2015/04

  31. Liイオン二次電池における充放電Li輸送と劣化現象のモデル解析

    山上達也, 高岸洋一

    R&D神戸製鋼技報　64　(2)　99-104　2014/10

  32. Numerical Simulation and direct observation of grain particle motion of slurry on running wire saw Peer-reviewed

    T. Yamaue, H. Matsuoka, Y. Takagishi, S. Okada, K. Nakayama, K. Yoshikawa, H. Suwabe

    the Proceeding of 21st International Photovoltaic Science and Engineering Conference　2011

  33. Impurity effects on nucleation of supercooled Lennard-Jones melt: A molecular dynamics study Peer-reviewed

    Y. Takagishi, T. Kubo, T. Nakada

    Journal of Crystal Growth　311　(3)　670-674　2009/01

    Publisher: Elsevier BV

    DOI： 10.1016/j.jcrysgro.2008.09.045 　

    ISSN： 0022-0248

  34. Systematic study of solidification of Lennard-Jones melts including an impurity molecule Peer-reviewed

    Y. Takagishi, T. Kubo, H. Hondoh, T. Nakada

    Journal of Crystal Growth　310　(7-9)　1349-1354　2008/04

    Publisher: Elsevier BV

    DOI： 10.1016/j.jcrysgro.2007.11.180 　

    ISSN： 0022-0248

  35. Orientation of para-phenylene crystals nucleated on alkali-halide stepped substrates Peer-reviewed

    H. Yamamoto, H. Takahashi, N. Yano, Y. Takagishi, T. Kubo, T. Harami, H. Hondoh, T. Nakada

    Journal of Crystal Growth　310　(7-9)　1729-1733　2008/04

    Publisher: Elsevier BV

    DOI： 10.1016/j.jcrysgro.2007.11.129 　

    ISSN： 0022-0248

  36. 過冷却融液中の核形成における不純物効果 –分子動力学による考察

    高岸洋一, 久保貴資, 中田俊隆

    立命館大学理工学研究所紀要　66　69　2007

  37. 不純物分子がLennard-Jones融液の結晶化に与える影響

    高岸洋一, 本同宏成, 久保貴資, 中田俊隆

    立命館大学理工学研究所紀要　65　(99)　99　2006

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Misc. 6

  1. リチウムイオン電池電極の製造プロセス・シミュレーション

    高岸洋一, 北川勇人

    こべるにくす　58　11-14　2024

  2. リチウムイオン２次電池の安全性試験シミュレーション

    高岸洋一, 山上達也

    車載テクノロジー　8　(1)　29　2020/11

  3. リチウムイオン二次電池向けシミュレーション技術

    山上達也, 高岸洋一, 岡部洋輔

    月刊JETI　(11)　42　2017/11

  4. 二次電池安全性評価における先端CAE技術の活用

    高岸洋一, 山中拓己

    こべるにくす　27　1-4　2017/10

  5. 車両走行を想定したリチウムイオン電池モジュールの劣化シミュレーション

    高岸洋一, 山上達也

    こべるにくす　25　9-12　2014/10

  6. 5.ナノ粒子・ナノ構造(「The 15th International Conference on Crystal Growth (ICCG-15) in conjunction with The 13th Conference on Vapor Growth and Epitaxy and The US Biennial Workshop on Organometallic Vapor Phase Epitaxy」報告)

    高岸洋一

    日本結晶成長学会誌　34　(3)　169　2007

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Books and Other Publications 8

  1. 塗工（塗布・乾燥）プロセスの基礎と実践～電池電極への展開/トラブル対策/シミュレーション～

    高岸洋一, 馬場良平

    R＆D支援センター　2025/12

  2. プロセスインフォマティクス

    情報機構　2024/06

    ISBN: 9784865022704

  3. 車載用リチウムイオン電池の開発と市場 2024

    高岸洋一

    シーエムシー出版　2023/09

  4. 車載用リチウムイオン電池のリユース技術と実際例

    高岸洋一

    エヌ・ティー・エス　2023/08

  5. リチウムイオン電池の長期安定利用に向けたマネジメント技術

    高岸洋一, 山中拓己, 山上達也

    技術情報協会　2023/02

  6. 車室内空間の快適性向上と材料・空間設計技術

    高岸洋一, 緒方剛

    技術情報協会　2023

  7. AI・MI・計算科学を活用した蓄電池研究開発動向

    高岸洋一

    S&T出版　2021/06

  8. リチウムイオン電池の分析、解析と評価技術 事例集

    高岸洋一

    技術情報協会　2019

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Presentations 56

  1. マルチモーダル計測とローカルLLMを組み合わせた電子ビーム積層造形プロセスの自律最適化

    高岸 洋一, 山崎 雅広, 北村 真一, 千葉 晶彦

    日本Additive Manufacturing学会 第2回講演大会　2026/09

  2. Intelligent System for Quality Assessment in EBM Additive Manufacturing using Deep Learning and Large Language Model Agent

    Yoichi Takagishi, Toshi-Taka Ikeshoji, Akihiko Chiba

    2nd International Conference on Future of AM 2026　2026/08

  3. AI×マルチフィジックス計算で切り開く 金属積層造形プロセス・モデリングの新展開

    高岸 洋一

    KESCO特別セミナー　2026/06

  4. ⾦属粉末床の溶融凝固MD計算と LLMによる仮説⽣成

    高岸 洋一, 池庄司敏孝, 柳原圭司, 千葉晶彦

    計算工学講演会論文集 Vol.31 B-05-01　2026/06

  5. 金属積層造形における数値計算データからの 物理的意味の自動抽出

    高岸洋一, 池庄司敏孝, 千葉晶彦

    計算工学講演会論文集 Vol.31 E-06-04　2026/06

  6. 分子動力学計算とCV分析による 金属粉末床の溶融凝固プロセス仮説生成

    高岸 洋一, 池庄司敏孝, 柳原圭司, 千葉晶彦

    粉体粉末冶金協会 第137回講演大会　2026/05

  7. Database construction of EBM additive manufacturing and intelligent quality judge model using LLMs (2)

    Yoichi Takagishi, Yusaku Sato, Shoichi Sato, Toshi-Taka Ikeshoji, Akihiko Chiba

    2026/03/13

  8. データ駆動で進化するEBM積層造形：DB・AI・シミュレーションによる知能化と現象解明へのアプローチ

    高岸洋一池庄司敏孝, 千葉晶彦

    第4回 AM Worldセミナー　2026/02

  9. Multi-scale modeling of metal powder melting/solidification process using surrogate model

    Yoichi Takagishi, Akihiko Chiba

    2025/12/17

  10. 物理モデルおよび機械学習を用いたバッテリー劣化予測技術 Invited

    高岸洋一

    電気化学会関西支部 第65回電気化学セミナー「蓄電池の資源循環プロセスの実現に向けた技術開発 ～寿命診断・劣化解析技術、２次利用、リサイクル～」　2025/07/17

  11. CAE分野における大規模言語モデルの活用

    高岸 洋一

    日本計算工学会 第13回公設試シンポジウム　2024/11/28

  12. LLM と汎用CAE ソフトの接続による解釈可能な最適化プロセスの検討

    高岸洋一, 山上達也

    第37 回計算力学講演会　2024/10/18

  13. Multi-Physics simulations of Li growth in solid electrolytes with different pressing conditions

    Yoichi Takagishi, Hidemasa Tsuneishi, Takashi Achiha, Takuya Mori, Tatsuya Yamaue

    International Meeting on Lithium Batteries (iMLB2024)　2024/06/20

  14. Text to CAE; Physics-based modeling and discussion with Large Language Model

    Yoichi Takagishi, Tatsuya Yamaue

    2024/06

  15. あいちSR施設におけるCu K-edgeエネルギーシフトと各計測時系列データとの相関分析

    高岸洋一, 渡部孝, 岡島敏浩

    シンクロトロン光研究センターシンポジウム　2024/01/15

  16. 固体電解質界面におけるLi析出シミュレーション

    高岸洋一, 常石英雅, 小西遼河, 阿知波敬, 大西優輝, 森拓弥, 山上達也

    第64回電池討論会要旨集　2023/11

  17. リチウムイオン電池製造工程モデリングと性能予測

    高岸洋一, 山中拓己, 山上達也

    第64回電池討論会要旨集　2023/11

  18. Generative Modeling of 2D-SEM images of degraded SiOx electrode using Variational Auto-Encoder

    Yoichi Takagishi, Takumi Yamanaka, Tatsuya Yamaue

    13th Advanced Automotive Battery Conference Europe 2023　2023/06/19

  19. 変分自己符号化器を用いたSi系負極断面の劣化状態生成

    高岸洋一, 山中拓己, 山上達也

    第63回電池討論会要旨集　2022/11

  20. Electrochemistry-based Simulation of Multiple dendrites for Li-ion Battery

    Yoichi Takagishi, Tatsuya Yamaue

    12th Advanced Automotive Battery Conference Europe　2022/06/13

  21. LIB負極を想定したリチウム析出の数理モデル構築

    高岸洋一, 山中拓己, 松田祐樹, 山上達也

    第62回電池討論会要旨集　2021/11

  22. リチウムイオン電池の劣化予測技術

    高岸洋一

    第30回神奈川大学オープンラボ　2021/10/29

  23. Machine learning based modeling for ride comfort of passenger vehicle on public roads in Thailand

    Yoichi Takagishi, Koji Kamimura, Takamasa Sunami, Noriyuki Kobayashi, Atsuya Yamamoto

    2021 JSAE Annual Congress (Spring) - Summarized Paper　2021/05

  24. 電池モジュールの加熱・延焼モデリングと設計最適化

    高岸洋一, 山中拓己, 山上達也

    第61回電池討論会要旨集　2020/11/20

  25. メゾ構造を考慮したLIB用電解液の泳動拡散解析と構造最適化

    高岸洋一, 山上達也

    計算機科学研究会　2020/10/23

  26. 機械学習を用いたリチウムイオン電池の劣化モデリング

    高岸洋一

    142回表面技術協会シンポジウム　2020/09/10

  27. 二次電池劣化予測技術の新展開

    高岸洋一

    科学技術交流財団研究会事業 二次電池の寿命予測技術・システム開発研究会　2020/07/14

  28. Diffusion-Reaction Dynamics Deduced from Sparse Identification of 2D Contour Data

    Yoichi Takagishi, Takashi Segi, Tatsuya Yamaue

    The 7th Asian Materials Data Symposium　2020/03/02

  29. Burning Test Simulation of Battery Module and its Design Optimization with Machine Learning

    Yoichi Takagishi, Tatsuya Yamaue

    20th Advanced Automotive Battery Conference virtual　2020/01/12

  30. 機械学習を用いたリチウムイオン電池劣化モデリングと電池パック挙動予測への展開-2

    高岸洋一, 山中拓巳, 坪田隆之, 山上達也

    第60回電池討論会要旨集　2019/11

  31. Degradation Modeling Based on FIB-SEM Image Features Extracted by Deep Neural Network

    Yoichi Takagishi, Tatsuya Yamaue

    Advanced Automotive Battery Conference Asia　2019/10/30

  32. 機械学習によるバッテリーの劣化・安全性のモデル化と解析技術のご紹介

    高岸洋一

    アドバンスト・バッテリー技術研究会　2019/05/30

  33. 機械学習を用いたリチウムイオン電池劣化モデリングと電池パック挙動予測への展開

    高岸洋一, 林 良樹, 山中拓巳, 坪田隆之, 山上達也

    第59回電池討論会要旨集　2018/11

  34. Designing mesoscale structures of Li-ion battery electrode using FIB-SEM image via machine learning

    Yoichi Takagishi, Tatsuya Yamaue

    The 9th International Conference on Multiscale Materials Modeling　2018/11/01

  35. Machine Learning Approaches for Designing Meso-scale Structure of Porous Materials Invited

    Yoichi Takagishi, Takumi Yamanaka, Takashi Segi, Tatsuya Yamaue

    The 19th KIM-JIM Symposium　2018/10/25

  36. カーボン担持体上のPt粒子の粗大化シミュレーション

    高岸洋一, 松尾修司, 山上達也

    第58回電池討論会要旨集　2017/11

  37. 断面SEM像に基づく電極合剤のミクロな圧壊・短絡シミュレーション

    高岸洋一, 山中拓巳, 坪田隆之, 山上達也

    第58回電池討論会要旨集　2017/11

  38. 疑似3次元モデルによるリチウムイオン電池の充放電・劣化シミュレーション

    高岸洋一

    アドバンスト・バッテリー技術研究会　2017/03/15

  39. 擬似3次元モデルによるリチウムイオン電池の充放電・劣化シミュレーション

    高岸洋一, 山中拓巳, 坪田隆之, 山上達也

    第57回電池討論会要旨集　2016

  40. 多因子劣化モデルによる走行時のLIB劣化予測シミュレーション

    高岸洋一, 山上達也

    自動車技術会 2021年春季大会学術講演会講演予稿集　2016/05

  41. 断面観察像を用いたリチウムイオン電池の充放電・劣化シミュレーション

    高岸洋一, 山中拓巳, 世木隆, 坪田隆之, 山上達也

    第56回電池討論会要旨集　2015/11

  42. Advanced Simulation Technologies for Li-ion Battery R&D

    Yoichi Takagishi, Tatsuya yamaue, Hideji Takahashi

    Batteries Event 2015　2015/10/08

  43. 中長期劣化を考慮したリチウムイオン電池のサイクル劣化シミュレーション

    高岸洋一, 山上達也, 坪田隆之

    第55回電池討論会要旨集　2014/11

  44. 40年供用後の吊橋主ケーブルの送気シミュレーション

    橋田芳朗, 峰地慎一, 高岸洋一, 福元裕彦, 出雲真仁, 山下恭敬, 坂田裕彦, 松田哲夫

    第69回土木学会

  45. MD計算によるナノメッシュ薄膜の熱伝導評価

    高岸洋一, 岡田勝吾, 福元裕彦

    日本伝熱シンポジウム講演論文集　2014/05

  46. リチウムイオン電池の発熱・劣化シミュレーション

    高岸洋一, 岡田勝吾, 坪田隆之, 西内万聡, 山上達也

    第53回電池討論会要旨集　2012/11

  47. 3次元凝集構造モデルによるリチウムイオン電池のサイクル特性解析

    高岸洋一, 山上達也, 坪田俊之, 世木隆

    第52回電池討論会要旨集　2011/11

  48. 多次元多相流モデルによるリチウムイオン電池の交流インピーダンス数値解析

    高岸洋一, 山上達也, 今北毅, 川野聡恭

    第51回電池討論会要旨集　2010/10

  49. 多次元多相流モデルによるリチウムイオン電池の充放電シミュレーション

    山上達也, 高岸洋一, 仲山公規, 今北毅, 土井謙太郎, 川野聡恭

    第50回電池討論会

  50. 不純物サイズの違いが成長カイネティクスに与える影響

    高岸洋一, 平井豪, 中田俊隆

    日本物理学会 2009 年春季大会　2009/03/28

  51. サクシノニトリル一高分子系における固液界面パターンの研究

    高岸洋一, 小山義昭, 中田俊隆

    日本物理学会 2008年秋季大会　2008/09/20

  52. Lennard-Jones過冷却融液からの核形成過程における不純物効果

    高岸洋一, 久保貴資, 中田俊隆

    日本物理学会2008年秋季大会　2008/03/24

  53. 不純物分子を含むLennard-Jones(111)面融液成長メカニズム

    高岸洋一, 久保貴資, 本同宏成, 中田俊隆

    日本物理学会2007年秋季大会　2007/09/19

  54. 不純物分子を含むLecmard-Jones融液の核形成シミュレーション

    高岸洋一, 久保貴資, 中田俊隆

    日本物理学会2007年春季大会　2007/03

  55. 疎媒性および親媒性不純物を含むLennard-Jones融液の成長メカニズム

    高岸洋一, 久保貴資, 本同宏成, 中田俊隆

    第36回結晶成長国内会議　2006/11

  56. Lennard-Jones融液成長における不純物分子形状と成長速度変化の関係

    高岸洋一, 本同宏成, 久保貴資, 中田俊隆

    日本物理学会2006年秋季大会　2006/09

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