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A Physics-driven Neural Networks-based Simulation System (PhyNNeSS) for multimodal interactive virtual environments

Suvranu De1, Dhannanjay Deo, Ganesh Sankaranarayanan

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Summary
This summary is machine-generated.

A new Physics-driven Neural Networks-based Simulation System (PhyNNeSS) enables real-time simulation of nonlinear deformable objects. This system achieves high update rates for realistic haptic feedback in applications like surgical training.

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Area of Science:

  • Computational mechanics
  • Real-time simulation
  • Machine learning in physics

Background:

  • Real-time graphics require 30 Hz updates, but haptics necessitate ~1 kHz.
  • Simulating nonlinear deformable objects at high rates with complex material properties is challenging.
  • Existing solutions lack generality for arbitrary nonlinearities.

Purpose of the Study:

  • To introduce PhyNNeSS, a Physics-driven Neural Networks-based Simulation System.
  • To overcome limitations in real-time simulation of nonlinear deformable objects.
  • To enable high-fidelity haptic feedback in interactive systems.

Main Methods:

  • Off-line pre-computation generates a database from finite element models.
  • Data is condensed into Radial Basis Function Network (RBFN) coefficients.
  • Neural networks reconstruct deformation fields and interaction forces in real-time.

Main Results:

  • Realistic simulations demonstrated for interactive surgical simulation with force feedback.
  • Developed models for a deformable human stomach and a Penrose-drain (FLS).
  • Scalability and accuracy control shown via error analysis based on neuron count.

Conclusions:

  • PhyNNeSS provides a unique system for real-time simulation of nonlinear deformable objects.
  • Physics-based pre-computation enables neural network training for real-time use.
  • The system is scalable and accurate, integrated into SoFMIS for general applications.