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Timon Rabczuk

2003–2026 年に発表

249
論文数
38,290
被引用数
98
h 指数
189
i10 指数

被引用数

Timon Rabczuk の年別被引用数1966 年: 被引用 2 件2001 年: 被引用 1 件2004 年: 被引用 4 件2005 年: 被引用 4 件2006 年: 被引用 17 件2007 年: 被引用 36 件2008 年: 被引用 40 件2009 年: 被引用 50 件2010 年: 被引用 43 件2011 年: 被引用 57 件2012 年: 被引用 81 件2013 年: 被引用 115 件2014 年: 被引用 205 件2015 年: 被引用 117 件2016 年: 被引用 227 件2017 年: 被引用 281 件2018 年: 被引用 305 件2019 年: 被引用 420 件2020 年: 被引用 517 件2021 年: 被引用 613 件2022 年: 被引用 856 件2023 年: 被引用 591 件2024 年: 被引用 672 件2025 年: 被引用 591 件2026 年: 被引用 193 件1967〜2000 年は被引用が無いため表示していません2002〜2003 年は被引用が無いため表示していません

引用元

国・地域

この著者を引用した国・地域の世界地図中国: 引用元論文 1,068 件、この内訳の 19.6%アメリカ合衆国: 引用元論文 654 件、この内訳の 12%ドイツ: 引用元論文 382 件、この内訳の 7%イギリス: 引用元論文 259 件、この内訳の 4.8%インド: 引用元論文 248 件、この内訳の 4.5%イラン: 引用元論文 191 件、この内訳の 3.5%ベトナム: 引用元論文 190 件、この内訳の 3.5%オーストラリア: 引用元論文 184 件、この内訳の 3.4%韓国: 引用元論文 164 件、この内訳の 3%イタリア: 引用元論文 162 件、この内訳の 3%フランス: 引用元論文 113 件、この内訳の 2.1%スペイン: 引用元論文 98 件、この内訳の 1.8%
0%19.6%その他 31.8%

機関

分野

  • Engineering54.9%
  • Computer Science14.2%
  • Physics and Astronomy9.8%
  • Materials Science5.9%
  • Medicine4.3%
  • Mathematics2.6%
  • その他8.3%

トピック

  • Numerical methods in engineering8.2%
  • Advanced Numerical Analysis Techniques6.3%
  • Model Reduction and Neural Networks4.2%
  • Advanced Numerical Methods in Computational Mathematics3.3%
  • Composite Structure Analysis and Optimization2.4%
  • Computational Geometry and Mesh Generation2.3%
  • その他73.3%

共著者

全論文

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  1. An Energy Approach to the Solution of Partial Differential Equations in Computational Mechanics via Machine Learning: Concepts, Implementation and Applications

    著者: Esteban Samaniego, Cosmin Anitescu, Somdatta Goswami, Vien Minh Nguyen-Thanh, Hongwei Guo, Khader M. Hamdia, Timon Rabczuk, Xiaoying Zhuang - Computer Methods in Applied Mechanics and Engineering 2020 被引用: 1,947

  2. A physics-informed neural network technique based on a modified loss function for computational 2D and 3D solid mechanics

    著者: Jinshuai Bai, Timon Rabczuk, Ashish Gupta, Laith Alzubaidi, Yuantong Gu - Computational Mechanics 2022 被引用: 237

  3. Kolmogorov–Arnold-Informed neural network: A physics-informed deep learning framework for solving forward and inverse problems based on Kolmogorov–Arnold Networks

    著者: Yizheng Wang, Jia Sun, Jinshuai Bai, Cosmin Anitescu, Mohammad Sadegh Eshaghi, Xiaoying Zhuang, Timon Rabczuk, Yinghua Liu - Computer Methods in Applied Mechanics and Engineering 2024 被引用: 154

  4. Kolmogorov Arnold Informed neural network: A physics-informed deep learning framework for solving PDEs based on Kolmogorov Arnold Networks

    著者: Yizheng Wang, Jia Sun, Jinshuai Bai, Cosmin Anitescu, Mohammad Sadegh Eshaghi, Xiaoying Zhuang, Timon Rabczuk, Yinghua Liu - arXiv (Cornell University), CoRR 2024 被引用: 51

  5. State of the Art of Machine Learning Models in Energy Systems, a Systematic Review

    著者: Amir Mosavi, Mohsen Salimi, Sina Ardabili, Timon Rabczuk, Shahaboddin Shamshirband, Annamária R. Várkonyi-Kóczy - Energies 2019 被引用: 555

  6. Towards Risk-Free Trustworthy Artificial Intelligence: Significance and Requirements

    著者: Laith Alzubaidi, Aiman Al-Sabaawi, Jinshuai Bai, Ammar Dukhan, Ahmed H. Alkenani, Ahmed Al‐Asadi, Haider A. Alwzwazy, Mohamed Manoufali, Mohammed A. Fadhel, A. S. Albahri, Catarina Moreira, Chun Ouyang, Jinglan Zhang, José Santamaría, Asma Salhi, Freek Hollman, Ashish Gupta, Ye Duan, Timon Rabczuk, Amin Abbosh, Yuantong Gu - International Journal of Intelligent Systems, Int. J. Intell. Syst. 2023 被引用: 80

  7. A deep energy method for finite deformation hyperelasticity

    著者: Vien Minh Nguyen‐Thanh, Xiaoying Zhuang, Timon Rabczuk - European Journal of Mechanics - A/Solids 2019 被引用: 302

  8. Isogeometric analysis: An overview and computer implementation aspects

    著者: Vinh Phu Nguyen, Cosmin Anitescu, Stéphane P. A. Bordas, Timon Rabczuk - Mathematics and Computers in Simulation, Math. Comput. Simul. 2015 被引用: 705

  9. Artificial Neural Network Methods for the Solution of Second Order Boundary Value Problems

    著者: Cosmin Anitescu, Elena Atroshchenko, Naif Alajlan, Timon Rabczuk - Computers, materials & continua/Computers, materials & continua (Print) 2019 被引用: 660

  10. An efficient optimization approach for designing machine learning models based on genetic algorithm

    著者: Khader M. Hamdia, Xiaoying Zhuang, Timon Rabczuk - Neural Computing and Applications, Neural Comput. Appl. 2020 被引用: 300

  11. Meshless methods: A review and computer implementation aspects

    著者: Vinh Phu Nguyen, Timon Rabczuk, Stéphane P. A. Bordas, Marc Duflot - Mathematics and Computers in Simulation, Math. Comput. Simul. 2008 被引用: 1,151

  12. A Survey of Deep Learning Techniques: Application in Wind and Solar Energy Resources

    著者: Shahab Shamshirband, Timon Rabczuk, Kwok-Wing Chau - IEEE Access 2019 被引用: 305

  13. Parametric deep energy approach for elasticity accounting for strain gradient effects

    著者: Vien Minh Nguyen‐Thanh, Cosmin Anitescu, Naif Alajlan, Timon Rabczuk, Xiaoying Zhuang - Computer Methods in Applied Mechanics and Engineering 2021 被引用: 220

  14. Kolmogorov–Arnold-Informed Neural Network: A Physics-Informed Deep Learning Framework for Solving Pdes Based on Kolmogorov–Arnold Networks

    著者: Yizheng Wang, Jia Sun, Jinshuai Bai, Cosmin Anitescu, Mohammad Sadegh Eshaghi, Xiaoying Zhuang, Timon Rabczuk, Yinghua Liu - 2024 被引用: 49

  15. COVID-19 Outbreak Prediction with Machine Learning

    著者: Sina Ardabili, Amir Mosavi, Pedram Ghamisi, Ferdinánd Filip, Annamária R. Várkonyi-Kóczy, Uwe Reuter, Timon Rabczuk, Peter M. Atkinson - Algorithms 2020 被引用: 360

  16. Variational Physics-informed Neural Operator (VINO) for solving partial differential equations

    著者: Mohammad Sadegh Eshaghi, Cosmin Anitescu, Manish Thombre, Yizheng Wang, Xiaoying Zhuang, Timon Rabczuk - Computer Methods in Applied Mechanics and Engineering 2025 被引用: 141

  17. Deep autoencoder based energy method for the bending, vibration, and buckling analysis of Kirchhoff plates with transfer learning

    著者: Xiaoying Zhuang, Hongwei Guo, Naif Alajlan, Hehua Zhu, Timon Rabczuk - European Journal of Mechanics - A/Solids 2021 被引用: 408

  18. Optimizing the neural network hyperparameters utilizing genetic algorithm

    著者: Saeid Nikbakht, Cosmin Anitescu, Timon Rabczuk - Journal of Zhejiang University. Science A 2021 被引用: 98

  19. Cracking particles: a simplified meshfree method for arbitrary evolving cracks

    著者: Timon Rabczuk, Ted Belytschko - International Journal for Numerical Methods in Engineering 2004 被引用: 1,694

  20. A Deep Collocation Method for the Bending Analysis of Kirchhoff Plate

    著者: Hongwei Guo, Xiaoying Zhuang, Timon Rabczuk - Computers, materials & continua/Computers, materials & continua (Print) 2019 被引用: 460

  21. Machine-learning-driven on-demand design of phononic beams

    著者: Liangshu He, Hongwei Guo, Yabin Jin, Xiaoying Zhuang, Timon Rabczuk, Yan Li - Science China Physics Mechanics and Astronomy 2021 被引用: 79

  22. Dual-horizon peridynamics: A stable solution to varying horizons

    著者: Huilong Ren, Xiaoying Zhuang, Timon Rabczuk - Computer Methods in Applied Mechanics and Engineering 2017 被引用: 649

  23. An isogeometric collocation method using superconvergent points

    著者: Cosmin Anitescu, Yue Jia, Yongjie Zhang, Timon Rabczuk - Computer Methods in Applied Mechanics and Engineering 2014 被引用: 126

  24. A level-set based IGA formulation for topology optimization of flexoelectric materials

    著者: Hamid Ghasemi, Harold S. Park, Timon Rabczuk - Computer Methods in Applied Mechanics and Engineering 2016 被引用: 392