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A Physics-Driven Neural Networks-based Simulation System (PhyNNeSS) for Multimodal Interactive Virtual Environments Involving Nonlinear Deformable Objects

机译:基于物理驱动的神经网络的仿真系统(PhyNNeSS),用于涉及非线性可变形对象的多模态交互式虚拟环境

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摘要

While an update rate of 30 Hz is considered adequate for real-time graphics, a much higher update rate of about I 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 Lapa-roscopic 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.
机译:虽然30 Hz的更新速率被认为足以用于实时图形,但对于触觉来说,大约1 kHz的更高更新速率是必需的。基于物理的可变形对象建模,特别是当涉及到较大的非线性变形和复杂的非线性材料属性时,以如此高的速率进行建模是实时仿真系统开发中最具挑战性的任务之一。虽然存在一些专门的解决方案,但是对于任意非线性还没有通用的解决方案。在这项工作中,我们介绍了PhyNNeSS(一种基于物理驱动的神经网络的仿真系统),以应对这一长期存在的技术挑战。第一步是离线预计算步骤,其中通过将仔细规定的位移应用于可变形对象的有限元模型的每个节点来生成数据库。在下一步中,将数据压缩为一组描述径向基函数网络(RBFN)神经元的系数。在实时计算过程中,这些神经网络用于重构变形场以及相互作用力。我们从具有实时力反馈的交互式手术模拟中提供了逼真的模拟示例。例如,我们开发了可变形的人体胃部模型和腹腔镜手术基础知识(FLS)训练工具箱中使用的Penrose引流模型。已经开发了一种独特的计算建模系统,该系统能够实时模拟非线性可变形物体的响应。该方法与以前的工作有所不同,它是基于系统的基于物理学的预计算步骤,可以训练可用于实时仿真的神经网络。通过仔细的错误分析,我们表明该方案是可扩展的,其准确性由模拟中使用的神经元数量控制。 PhyNNeSS已集成到SoFMIS(用于多模式交互式仿真的软件框架)中,以供一般使用。

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  • 来源
    《Presence》 |2011年第4期|p.289-308|共20页
  • 作者单位

    Center for Modeling, Simulation and Imaging in Medicine Rensselaer Polytechnic Institute Troy, New York 12180;

    Kitware Inc. Clifton Park, New York 12065;

    Center for Modeling, Simulation and Imaging in Medicine Rensselaer Polytechnic Institute Troy, New York 12180;

    Center for Modeling, Simulation and Imaging in Medicine Rensselaer Polytechnic Institute Troy, New York 12180;

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  • 入库时间 2022-08-18 00:39:37

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