Neural Computing and Applications

, Volume 31, Supplement 2, pp 1091–1101 | Cite as

Neural dynamics-based Poisson propagation for deformable modelling

  • Jinao ZhangEmail author
  • Yongmin Zhong
  • Julian Smith
  • Chengfan Gu
Original Article


This paper presents a new methodology from the standpoint of energy propagation for real-time and nonlinear modelling of deformable objects. It formulates the deformation process of a soft object as a process of energy propagation, in which the mechanical load applied to the object to cause deformation is viewed as the equivalent potential energy based on the law of conservation of energy and is further propagated among masses of the object based on the nonlinear Poisson propagation. Poisson propagation of mechanical load in conjunction with non-rigid mechanics of motion is developed to govern the dynamics of soft object deformation. Further, these two governing processes are modelled with cellular neural networks to achieve real-time computational performance. A prototype simulation system with a haptic device is implemented for real-time simulation of deformable objects with haptic feedback. Simulations, experiments as well as comparisons demonstrate that the proposed methodology exhibits nonlinear force–displacement relationship, capable of modelling large-range deformation. It can also accommodate homogeneous, anisotropic and heterogeneous materials by simply changing the constitutive coefficient value of mass points.


Deformable objects Nonlinear deformation Poisson equation Cellular neural networks Real-time performance 


Compliance with ethical standards

Conflict of interest

The authors declare that there is no conflict of interest.


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Copyright information

© The Natural Computing Applications Forum 2017

Authors and Affiliations

  1. 1.School of EngineeringRMIT UniversityBundooraAustralia
  2. 2.Department of Surgery, School of Clinical Sciences at Monash HealthMonash UniversityClaytonAustralia

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