CRII: CHS: A Plug-and-Play Deformable Model Based on Extended Domain Decomposition

CRII:CHS:基于扩展域分解的即插即用变形模型

基本信息

  • 批准号:
    1464306
  • 负责人:
  • 金额:
    $ 17.48万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2015
  • 资助国家:
    美国
  • 起止时间:
    2015-03-01 至 2018-02-28
  • 项目状态:
    已结题

项目摘要

Advances in data acquisition tools have led to a dramatic increase in the geometric complexity of 3D data. Efficiently modeling, simulating, and analyzing these scanned large-scale real-world models become a serious challenge, because the numerical integration of high dimensional partial differential equations (over millions of degrees of freedom) is prohibitive for time-critical applications such as surgical simulation, bio-medical imaging, virtual/augmented reality, and physically-based animation. The problem becomes significantly more acute in situations where the rest-shape geometries of the 3D models are frequently altered and there is a need for collision detection/response coupled with high fidelity visualization of heterogeneous material properties and efficient transmission over the network to facilitate collaborative interaction. In this project the PI will address this challenge by developing a research program to create a modularized computational framework for efficient deformable simulation by partitioning the deformable body into small-size domains and re-connecting them back using weakened linkages. Domain-level computations are independent and reusable; thus, the expensive deformable simulation is reframed as a plug-and-play computational assemblage just like playing with LEGO blocks, and orders of magnitude speedup can be obtained. The plug-and-play deformable model that will be the primary project outcome will advance state-of-the-art techniques in physical simulation, animation and visualization, and will also profoundly benefit a broad range of interdisciplinary fields that directly impact people in their daily lives, from the modeling and registration of deformable human organs for surgical simulation, to the analysis of roadway pavement stress, to silent speech recognition.The PI's approach pivots on the transformative concept of divide-and-conquer deformable model. Unlike most state-of-the-art techniques that simulate a deformable object in its entirely by means of a "one-stop" solver, the PI will develop innovative algorithms that break a simulation into independent computational modules, with the final result being obtained by incrementally assembling the local computations. The PI will seek theoretical solutions to two general questions: "how to smartly divide" and "how to effectively conquer" in the context of deformable simulation. In particular, he will investigates a theoretically grounded domain decomposition and coupling mechanism so that domain-level computation is independent, reusable, modularized and also a good fit with existing parallel computing architectures such as multi-core CPUs or GPUs. The PI will develop a new theory for the real-time spectral deformation processing that divides the simulation not only spatially but also spectrally, based on a power iteration and inertia analysis. He will also explore possible solutions to the problem of optimal domain partitioning, in which the simulation is parameterized geometrically and the most effective partition is obtained by solving a geometry optimization problem similar to the Voronoi diagram. As the test-bed for the aforementioned theoretical and algorithmic advances, the PI will develop a haptic-enabled collaborative digital fabrication system, which will ultimately allow multiple users, from distant sites to smoothly interact to design and craft physically simulated virtual objects, which can then be 3D printed if desired.
数据采集​​工具的进步导致 3D 数据的几何复杂性急剧增加。 有效地建模、模拟和分析这些扫描的大型现实世界模型成为一项严峻的挑战,因为高维偏微分方程(超过数百万自由度)的数值积分对于手术模拟等时间关键型应用来说是令人望而却步的、生物医学成像、虚拟/增强现实和基于物理的动画。 在 3D 模型的静止形状几何形状经常改变并且需要碰撞检测/响应以及异质材料属性的高保真可视化和通过网络的有效传输以促进协作交互的情况下,该问题变得更加严重。 在这个项目中,PI 将通过开发一个研究计划来解决这一挑战,通过将可变形体划分为小尺寸域并使用弱连接将它们重新连接起来,创建一个模块化计算框架,以进行有效的变形模拟。 领域级计算独立且可复用;因此,昂贵的变形模拟被重新构建为即插即用的计算组合,就像玩乐高积木一样,并且可以获得数量级的加速。 即插即用的变形模型将成为主要项目成果,将推进物理模拟、动画和可视化领域最先进的技术,也将深刻造福于直接影响人们生活的广泛跨学科领域。从用于手术模拟的可变形人体器官的建模和注册,到道路路面应力分析,再到无声语音识别,PI 的方法以分而治之的变形模型的变革概念为基础。 与大多数完全通过“一站式”求解器模拟可变形物体的最先进技术不同,PI 将开发创新算法,将模拟分解为独立的计算模块,并获得最终结果通过增量组装本地计算。 PI将在变形模拟的背景下寻求两个一般性问题的理论解决方案:“如何巧妙地划分”和“如何有效地征服”。 特别是,他将研究一种具有理论基础的领域分解和耦合机制,使领域级计算独立、可重用、模块化,并且能够很好地契合现有的并行计算架构,例如多核CPU或GPU。 PI 将开发一种用于实时光谱变形处理的新理论,该理论基于功率迭代和惯性分析,不仅在空间上而且在光谱上划分模拟。 他还将探索最优域划分问题的可能解决方案,其中模拟被几何参数化,并通过求解类似于Voronoi图的几何优化问题来获得最有效的划分。 作为上述理论和算法进步的测试平台,PI 将开发一种支持触觉的协作数字制造系统,该系统最终将允许来自遥远地点的多个用户顺利交互以设计和制作物理模拟的虚拟对象,这可以然后根据需要进行 3D 打印。

项目成果

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Yin Yang其他文献

LinSBFT: Linear-Communication One-Step BFT Protocol for Public Blockchains
LinSBFT:公共区块链线性通信一步 BFT 协议
  • DOI:
  • 发表时间:
    2020-07-15
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Xiaodong Qi;Yin Yang;Zhao Zhang;Cheqing Jin;Aoying Zhou
  • 通讯作者:
    Aoying Zhou
The development of methods for the detection of Salmonella in chickens by a combination of immunomagnetic separation and PCRs
免疫磁珠分离与PCR相结合的鸡沙门氏菌检测方法的开发
  • DOI:
    10.1002/bab.1539
  • 发表时间:
    2016-10-01
  • 期刊:
  • 影响因子:
    2.8
  • 作者:
    F. Dai;Miao Zhang;Dixin Xu;Yin Yang;Jiaxiao Wang;Mingzhen Li;Meihong Du
  • 通讯作者:
    Meihong Du
Interim Analysis of ZUMA-12: A Phase 2 Study of Axicabtagene Ciloleucel (Axi-Cel) as First-Line Therapy in Patients (Pts) With High-Risk Large B Cell Lymphoma (LBCL)
ZUMA-12 的中期分析:Axicabtagene Ciloleucel (Axi-Cel) 作为高危大 B 细胞淋巴瘤 (LBCL) 患者 (Pts) 一线治疗的 2 期研究
  • DOI:
    10.1182/blood-2020-134449
  • 发表时间:
    2020-11-05
  • 期刊:
  • 影响因子:
    20.3
  • 作者:
    S. Neelapu;M. Dickinson;M. Ulrickson;O. Oluwole;A. Herrera;C. Thieblemont;C. Ujjani;Yi Lin;P. Riedell;N. Kekre;S. Vos;Yin Yang;F. Milletti;L. Goyal;J. Kawashima;J. Chavez
  • 通讯作者:
    J. Chavez
The Association Between Rumination and Craving in Chinese Methamphetamine-Dependent Patients: The Masking Effect of Depression.
中国甲基苯丙胺依赖患者的沉思与渴望之间的关联:抑郁症的掩蔽效应。
  • DOI:
    10.1080/10826084.2024.2352617
  • 发表时间:
    2024-05-24
  • 期刊:
  • 影响因子:
    2
  • 作者:
    Xiuli Liu;Qingjie Tai;Feifei Meng;Yang Tian;Dongmei Wang;Fusheng Fan;Yin Yang;Fabing Fu;Dejun Wei;Shan Tang;Jiajing Chen;Yuxuan Du;R. Zhu;Wenjia Wang;Siying Liu;Jiaxue Wan;Wanni Zhang;Qilin Liang;Yuqing Li;Li Wang;Huixia Zhou;Xiangyang Zhang
  • 通讯作者:
    Xiangyang Zhang
Statistical data-based approach to establish risk criteria for cascade reservoir systems in China
基于统计数据的方法建立中国梯级水库系统的风险标准
  • DOI:
    10.1007/s12665-020-08951-2
  • 发表时间:
    2020-05-01
  • 期刊:
  • 影响因子:
    2.8
  • 作者:
    Cancan Wang;Q. Ren;Jianfang Zhou;Yin Yang
  • 通讯作者:
    Yin Yang

Yin Yang的其他文献

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{{ truncateString('Yin Yang', 18)}}的其他基金

CHS: Small: Towards Next-Generation Large-Scale Nonlinear Deformable Simulation
CHS:小型:迈向下一代大规模非线性变形模拟
  • 批准号:
    2244651
  • 财政年份:
    2022
  • 资助金额:
    $ 17.48万
  • 项目类别:
    Standard Grant
CAREER: Deep Learning Empowered Nonlinear Deformable Model
职业:深度学习赋能非线性变形模型
  • 批准号:
    2301040
  • 财政年份:
    2022
  • 资助金额:
    $ 17.48万
  • 项目类别:
    Continuing Grant
CHS: Small: High Resolution Motion Capture
CHS:小:高分辨率运动捕捉
  • 批准号:
    2008564
  • 财政年份:
    2020
  • 资助金额:
    $ 17.48万
  • 项目类别:
    Standard Grant
III: Small: Collaborative Research: Learning Active Physics-Based Models from Data
III:小:协作研究:从数据中学习基于物理的主动模型
  • 批准号:
    2008915
  • 财政年份:
    2020
  • 资助金额:
    $ 17.48万
  • 项目类别:
    Standard Grant
CHS: Small: Towards Next-Generation Large-Scale Nonlinear Deformable Simulation
CHS:小型:迈向下一代大规模非线性变形模拟
  • 批准号:
    2016414
  • 财政年份:
    2019
  • 资助金额:
    $ 17.48万
  • 项目类别:
    Standard Grant
CHS: Small: Towards Next-Generation Large-Scale Nonlinear Deformable Simulation
CHS:小型:迈向下一代大规模非线性变形模拟
  • 批准号:
    2016414
  • 财政年份:
    2019
  • 资助金额:
    $ 17.48万
  • 项目类别:
    Standard Grant
CAREER: Deep Learning Empowered Nonlinear Deformable Model
职业:深度学习赋能非线性变形模型
  • 批准号:
    1845026
  • 财政年份:
    2019
  • 资助金额:
    $ 17.48万
  • 项目类别:
    Continuing Grant
CAREER: Deep Learning Empowered Nonlinear Deformable Model
职业:深度学习赋能非线性变形模型
  • 批准号:
    2011471
  • 财政年份:
    2019
  • 资助金额:
    $ 17.48万
  • 项目类别:
    Continuing Grant
CHS: Small: Towards Next-Generation Large-Scale Nonlinear Deformable Simulation
CHS:小型:迈向下一代大规模非线性变形模拟
  • 批准号:
    1717972
  • 财政年份:
    2017
  • 资助金额:
    $ 17.48万
  • 项目类别:
    Standard Grant

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CHS:小型:迈向下一代大规模非线性变形模拟
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    Standard Grant
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