Neural dynamics-based Poisson propagation for deformable modelling

Zhang, J, Zhong, Y, Smith, J and Gu, C 2019, 'Neural dynamics-based Poisson propagation for deformable modelling', Neural Computing and Applications, vol. 31, pp. 1091-1101.

Document type: Journal Article
Collection: Journal Articles

Title Neural dynamics-based Poisson propagation for deformable modelling
Author(s) Zhang, J
Zhong, Y
Smith, J
Gu, C
Year 2019
Journal name Neural Computing and Applications
Volume number 31
Start page 1091
End page 1101
Total pages 11
Publisher Springer
Abstract 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.
Subject Biomechanical Engineering
Keyword(s) Cellular neural networks
Deformable objects
Nonlinear deformation
Poisson equation
Real-time performance
DOI - identifier 10.1007/s00521-017-3132-3
Copyright notice © The Natural Computing Applications Forum 2017
ISSN 0941-0643
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 2 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 0 times in Scopus Article
Altmetric details:
Access Statistics: 33 Abstract Views  -  Detailed Statistics
Created: Mon, 29 Apr 2019, 13:04:00 EST by Catalyst Administrator
© 2014 RMIT Research Repository • Powered by Fez SoftwareContact us