Parameter identification for dynamic simulation

Real-time simulation of the nonlinear visco-elastic deformations of soft tissues

“…Parameter tuning has been achieved in earlier works through trial-and-error interactive adjustments until the simulated behavior is seemingly realistic, matching the experimentally measured response [39]. In few recent studies, automated procedures for parameter identification have been attempted using advanced optimization techniques, e.g., genetic algorithm [40], simulated annealing [41] or evolutionary minimization algorithm [42]. These approaches, however, are most beneficial if a large number of parameters are to be determined, e.g., for non-homogenous anisotropic models, and/or a large experimental data basis is available.…”

Simultaneous Topology and Stiffness Identification for Mass-Spring Models Based on FEM Reference Deformations

“…Neural networks are used for the simulation of dynamic MSMs by [9]. Furthermore, methods based on genetic algorithms for stiffness value determination were discussed in [4] and [6]. However, all the methods described above only work for predefined topologies such as rectangular or tetrahedral structures.…”

Acquisition of Elastic Models for Interactive Simulation

“…Reznik and Laugier (1996) tune the spring in their model of a rubber tip of a robotic finger according to the Young's modulus of rubber. Identification from observation for dynamic systems is described in Joukhadar, Garat and Laugier (1997). In Louchet, Provot and Crochemore (1995) a mass-spring cloth model is fit to the observation of hanging cloth behavior.…”

“…Medical robotics can also profit from user interfaces with haptic feedback (Sagar et al 1994). Other applications which involve elastic simulation are haptic interfaces to virtual worlds (Salisbury and Srinivasan 1997), and the design of robotic tasks involving soft materials (Joukhadar, Bard and Laugier 1994;Joukhadar, Garat and Laugier 1997).…”