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.
- Dynamic Systems and Control Division
Radial Basis Function Network (RBFN) Approximation of Finite Element Models for Real-Time Simulation
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Narayanan, MS, Singla, P, Garimella, S, Waz, W, & Krovi, V. "Radial Basis Function Network (RBFN) Approximation of Finite Element Models for Real-Time Simulation." Proceedings of the ASME 2011 Dynamic Systems and Control Conference and Bath/ASME Symposium on Fluid Power and Motion Control. ASME 2011 Dynamic Systems and Control Conference and Bath/ASME Symposium on Fluid Power and Motion Control, Volume 2. Arlington, Virginia, USA. October 31–November 2, 2011. pp. 799-806. ASME. https://doi.org/10.1115/DSCC2011-6154
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