Radial basis function network (rbfn) approximation of finite element models for real-time simulation

Madusudanan Sathia Narayanan, Puneet Singla, Sudha Garimella, Wayne Waz, Venkat Krovi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Nonlinearities inherent in soft-tissue interactions create roadblocks to realization of high-fidelity real-time haptics-based medical simulations. While finite element (FE) formulations offer greater accuracy over conventional spring-mass-network models, computational-complexity limits achievable simulation-update rates. Direct interaction with sensorized physical surrogates, in offline or online modes, allows a temporary sidestepping of computational issues but hinders parametric analysis and true exploitation of a simulation-based testing paradigm. Hence, in this paper, we develop Radial-Basis Neural-Network approximations, to FE-model data within a Modified Resource Allocating Network (MRAN) framework. Real-time simulation of the reduced order neural-network approximations at high temporal resolution provided the haptic-feedback. Validation studies are being conducted to evaluate the kinesthetic realism of these models with medical experts.

Original languageEnglish (US)
Title of host publicationASME 2011 Dynamic Systems and Control Conference and Bath/ASME Symposium on Fluid Power and Motion Control, DSCC 2011
Pages799-806
Number of pages8
DOIs
Publication statusPublished - Dec 1 2011
EventASME 2011 Dynamic Systems and Control Conference and Bath/ASME Symposium on Fluid Power and Motion Control, DSCC 2011 - Arlington, VA, United States
Duration: Oct 31 2011Nov 2 2011

Publication series

NameASME 2011 Dynamic Systems and Control Conference and Bath/ASME Symposium on Fluid Power and Motion Control, DSCC 2011
Volume2

Other

OtherASME 2011 Dynamic Systems and Control Conference and Bath/ASME Symposium on Fluid Power and Motion Control, DSCC 2011
CountryUnited States
CityArlington, VA
Period10/31/1111/2/11

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All Science Journal Classification (ASJC) codes

  • Fluid Flow and Transfer Processes
  • Control and Systems Engineering

Cite this

Narayanan, M. S., Singla, P., Garimella, S., Waz, W., & Krovi, V. (2011). Radial basis function network (rbfn) approximation of finite element models for real-time simulation. In ASME 2011 Dynamic Systems and Control Conference and Bath/ASME Symposium on Fluid Power and Motion Control, DSCC 2011 (pp. 799-806). (ASME 2011 Dynamic Systems and Control Conference and Bath/ASME Symposium on Fluid Power and Motion Control, DSCC 2011; Vol. 2). https://doi.org/10.1115/DSCC2011-6154