Radial Basis Function Network (RBFN) Approximation of Finite Element Models for Real-Time Simulation

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.