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Reproducing Kernel Particle Method for Solving Partial Differential Equations

  • Jiun Shyan Chen
  • , Wing Kam Liu
  • , Yanping Lian
  • , Miguel A. Bessa
  • , Michael C. Hillman
  • , Sheng Wei Chi
  • University of California at San Diego
  • Northwestern University
  • Pennsylvania State University
  • University of Illinois at Chicago

科研成果: 书/报告/会议事项章节 › 章节 › 同行评审

摘要

This chapter gives a state of the art review of the weak-form- and strong-form-based reproducing kernel particle method (RKPM). The construction of the continuous and discrete reproducing kernel (RK) approximations and their approximation properties are first presented. Recent developments and their connection to other meshfree methods, as well as an overview of the inherent multiresolution capabilities of the RKPM, are then given. The Galerkin-based method is discussed in detail, along with several recent advances in quadrature and stability. A review of strong form collocation with RK approximation as well as implicit gradient enhancement is presented. Formulations for large deformation and contact problems for which the RKPM is particularly well suited are also discussed.

源语言英语
主期刊名Encyclopedia of Computational Mechanics
出版商wiley
页1-44
页数44
ISBN(电子版)9781119176817
ISBN(印刷版)9781119003793
DOI
出版状态已出版 - 1 1月 2017
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