A problem specific recurrent neural network for the description and simulation of dynamic spring models

Author

Nürnberger, Andreas ; Radetzky, Arne ; Kruse, Rudolf

Author_Institution

Fac. of Comput. Sci., Magdeburg Univ. of Technol., Germany

Volume

1

fYear

1998

fDate

4-8 May 1998

Firstpage

468

Abstract

We present a recurrent neural network which was designed for the description and simulation of dynamic spring models. The network simulates the physical behavior of deformable or elastic solids like stiffness, viscosity and inertia. The physical parameters of the real model can be used to initialize the network parameters. Besides, it is possible to learn the deformation behavior of a real solid. Using a neural network structure, local changes to the system like collisions or cuts can be easily performed during simulation. Furthermore, it is possible to speed up the simulation by parallel hardware

Keywords

deformation; dynamics; elasticity; mechanical engineering computing; recurrent neural nets; simulation; deformation behavior; dynamic spring models; inertia; recurrent neural network; simulation; stiffness; viscosity; Computational modeling; Computer science; Computer simulation; Deformable models; Hardware; Neural networks; Recurrent neural networks; Solid modeling; Springs; Viscosity;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on

Conference_Location

Anchorage, AK

ISSN

1098-7576

Print_ISBN

0-7803-4859-1

Type

conf

DOI

10.1109/IJCNN.1998.682312

Filename

682312