Universal approximation using radial-basis-function networks
Neural Computation
Approximation and radial-basis-function networks
Neural Computation
A Recursive Orthogonal Least Squares Algorithm for Training RBF Networks
Neural Processing Letters
NeuroAnimator: fast neural network emulation and control of physics-based models
Proceedings of the 25th annual conference on Computer graphics and interactive techniques
Mathematics and Computers in Simulation
Reconstruction and representation of 3D objects with radial basis functions
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
Scanning physical interaction behavior of 3D objects
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
Inverse Finite Element Characterization of Soft Tissues
MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
Measuring Just Noticeable Differences for Haptic Force Feedback: Implications for Rehabilitation
HAPTICS '02 Proceedings of the 10th Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems
Non-linear anisotropic elasticity for real-time surgery simulation
Graphical Models - Special issue on SMI 2002
Haptics in Minimally Invasive Surgical Simulation and Training
IEEE Computer Graphics and Applications
Metamodeling using extended radial basis functions: a comparative approach
Engineering with Computers
Finite Elements in Analysis and Design
Physically realistic virtual surgery using the point-associated finite field (PAFF) approach
Presence: Teleoperators and Virtual Environments - Special issue: Virtual heritage
Neural Networks for Applied Sciences and Engineering
Neural Networks for Applied Sciences and Engineering
Mesh deformation based on radial basis function interpolation
Computers and Structures
Efficient Point-Based Rendering Techniques for Haptic Display of Virtual Objects
Presence: Teleoperators and Virtual Environments
WHC '07 Proceedings of the Second Joint EuroHaptics Conference and Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems
SCA '07 Proceedings of the 2007 ACM SIGGRAPH/Eurographics symposium on Computer animation
Regularization in the selection of radial basis function centers
Neural Computation
Contact Model for Haptic Medical Simulations
ISBMS '08 Proceedings of the 4th international symposium on Biomedical Simulation
Data-Driven Haptic Rendering—From Viscous Fluids to Visco-Elastic Solids
IEEE Transactions on Haptics
Capture and modeling of non-linear heterogeneous soft tissue
ACM SIGGRAPH 2009 papers
PhyNeSS: A Physics-driven Neural Networks-based Surgery Simulation system with force feedback
WHC '09 Proceedings of the World Haptics 2009 - Third Joint EuroHaptics conference and Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems
Neural network constitutive model for rate-dependent materials
Computers and Structures
Vector field approximation by model inclusive learning of neural networks
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
A Cellular Neural Network Methodology for Deformable Object Simulation
IEEE Transactions on Information Technology in Biomedicine
Vehicle crash modelling using recurrent neural networks
Mathematical and Computer Modelling: An International Journal
On the efficiency of the orthogonal least squares training method for radial basis function networks
IEEE Transactions on Neural Networks
Enriching coarse interactive elastic objects with high-resolution data-driven deformations
EUROSCA'12 Proceedings of the 11th ACM SIGGRAPH / Eurographics conference on Computer Animation
Enriching coarse interactive elastic objects with high-resolution data-driven deformations
Proceedings of the ACM SIGGRAPH/Eurographics Symposium on Computer Animation
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While an update rate of 30 Hz is considered adequate for real-time graphics, a much higher update rate of about 1 kHz is necessary for haptics. Physics-based modeling of deformable objects, especially when large nonlinear deformations and complex nonlinear material properties are involved, at these very high rates is one of the most challenging tasks in the development of real-time simulation systems. While some specialized solutions exist, there is no general solution for arbitrary nonlinearities. In this work we present PhyNNeSS-a Physics-driven Neural Networks-based Simulation System-to address this long-standing technical challenge. The first step is an offline precomputation step in which a database is generated by applying carefully prescribed displacements to each node of the finite element models of the deformable objects. In the next step, the data is condensed into a set of coefficients describing neurons of a Radial Basis Function Network (RBFN). During real-time computation, these neural networks are used to reconstruct the deformation fields as well as the interaction forces. We present realistic simulation examples from interactive surgical simulation with real-time force feedback. As an example, we have developed a deformable human stomach model and a Penrose drain model used in the Fundamentals of Laparoscopic Surgery (FLS) training tool box. A unique computational modeling system has been developed that is capable of simulating the response of nonlinear deformable objects in real time. The method distinguishes itself from previous efforts in that a systematic physics-based precomputational step allows training of neural networks which may be used in real-time simulations. We show, through careful error analysis, that the scheme is scalable, with the accuracy being controlled by the number of neurons used in the simulation. PhyNNeSS has been integrated into SoFMIS (Software Framework for Multimodal Interactive Simulation) for general use.