Big Data for Fast and Accurate Numerical Simulation of Mechanical Structures

大数据用于快速准确的机械结构数值模拟

基本信息

  • 批准号:
    RGPIN-2017-05524
  • 负责人:
  • 金额:
    $ 2.04万
  • 依托单位:
  • 依托单位国家:
    加拿大
  • 项目类别:
    Discovery Grants Program - Individual
  • 财政年份:
    2022
  • 资助国家:
    加拿大
  • 起止时间:
    2022-01-01 至 2023-12-31
  • 项目状态:
    已结题

项目摘要

Numerical simulations of physical phenomena such as large and small deformations are a crucial tool for everything from building design to 3D printing. The knowledge of how something will perform in the real-world has a tremendous impact on the design process. However, even today, state-of-the-art algorithms are still several orders of magnitude too slow to be used interactively, especially when we consider constraints imposed by desired accuracy and computational challenges introduced by the high-resolution, multi-material nature of advanced additive manufacturing techniques. The problem becomes more daunting when one considers that next-generation interactive design tools for buildings, airplanes, cars and even characters in blockbuster films desire "in-the-loop" simulation. Such a setup has two principal benefits; first, designers can receive feedback on the effect of design changes instantaneously and second, ultra-fast simulation opens the door to intelligent, optimization-based suggestion schemes -- ones which can perform background exploration of the design space in order to find non-intuitive designs which satisfy designer constraints. Currently, numerical simulations are treated as disposable, thrown away once the desired structural analysis or animation has been completed. But why should this be the case ? What could we do with a large database of simulation data? Could we use it to accelerate a broad range of simulations without requiring the tedious and expensive precomputation on a case-by-case basis? In this research project I will explore the implications of this question and develop simulation algorithms which use prior information extracted from such a database to avoid the performance/fidelity trade-offs of traditional methods. Such algorithms could have a plethora of benefits for any domain in which physical simulation is used. In order to do this I will focus on three main areas1.) Compact, geometry independent representations for storing simulation data2.) Using stored data for fast, runtime numerical coarsening3.) Algorithms and devices with which to quickly and accurately capture material and geometry parameters necessary for simulation4.) New algorithms for solving coupled systems of linear and nonlinear equations which exploit both of the above. Accomplishing these four goals will push us towards a new era of high-performance physics simulations driven by Big Data. Just as how online databases have revolutionized areas such as computer vision, I envision a similar change will occur in the numerical physics and computer animation communities. I believe that this work, essentially building the google image search for simulation data, is crucial for bringing this to fruition.
物理现象(例如大小变形)的数值模拟是从建筑设计到3D打印的所有事物的关键工具。关于事物在现实世界中如何表现的知识会对设计过程产生巨大影响。但是,即使在今天,最新的算法仍然是几个数量级,而无法交互式使用,尤其是当我们考虑由高分辨率,多物质性质性质引入的所需准确性和计算挑战所施加的约束时。当人们考虑到大片电影中的建筑物,飞机,汽车甚至角色的下一代交互式设计工具时,问题变得更加艰巨。这样的设置有两个主要利益。首先,设计师可以立即收到有关设计效果的反馈,其次,超快速模拟为基于智能的,基于优化的建议方案打开了大门,该计划可以对设计空间进行背景探索,以便找到满足设计师约束的非直觉设计。当前,一旦完成所需的结构分析或动画,数值模拟被视为一次性模拟。但是为什么会这样呢?我们可以使用大型模拟数据数据库做什么?我们可以使用它来加速广泛的模拟,而不需要逐案的繁琐且昂贵的预约?在该研究项目中,我将探讨该问题的含义,并开发模拟算法,这些算法使用从此类数据库中提取的先前信息来避免传统方法的性能/忠诚度权衡。对于使用物理模拟的任何领域,这种算法可能具有大量的好处。为此,我将重点关注三个主要领域。实现这四个目标将使我们进入由大数据驱动的高性能物理模拟的新时代。正如在线数据库如何彻底改变了计算机视觉之类的领域一样,我设想在数值物理和计算机动画社区中也会发生类似的变化。我认为,这项工作本质上是构建Google图像搜索模拟数据,对于实现这一目标至关重要。

项目成果

期刊论文数量(0)
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科研奖励数量(0)
会议论文数量(0)
专利数量(0)

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Levin, David其他文献

A meta-analysis reveals that operational parameters influence levels of antibiotic resistance genes during anaerobic digestion of animal manures
  • DOI:
    10.1016/j.scitotenv.2021.152711
  • 发表时间:
    2022-01-03
  • 期刊:
  • 影响因子:
    9.8
  • 作者:
    Flores-Orozco, Daniel;Levin, David;Cicek, Nazim
  • 通讯作者:
    Cicek, Nazim
Cage-free local deformations using green coordinates
使用绿色坐标的无笼局部变形
  • DOI:
    10.1007/s00371-010-0438-x
  • 发表时间:
    2010-06
  • 期刊:
  • 影响因子:
    3.5
  • 作者:
    Luo, Xiaonan;Levin, David;Li, Zheng;Deng, Zhengjie;Liu, Dingyuan
  • 通讯作者:
    Liu, Dingyuan
Between moving least-squares and moving least-l1
  • DOI:
    10.1007/s10543-014-0522-0
  • 发表时间:
    2015-09-01
  • 期刊:
  • 影响因子:
    1.5
  • 作者:
    Levin, David
  • 通讯作者:
    Levin, David
One Simple Intervention Begets Another: Let's Get the Gestational Age Right First
  • DOI:
    10.1007/s10995-016-2003-3
  • 发表时间:
    2016-09-01
  • 期刊:
  • 影响因子:
    2.3
  • 作者:
    Levin, Julia;Gurau, David;Levin, David
  • 通讯作者:
    Levin, David
Effect of substrate loading on hydrogen production during anaerobic fermentation by Clostridium thermocellum 27405
  • DOI:
    10.1007/s00253-006-0316-7
  • 发表时间:
    2006-09-01
  • 期刊:
  • 影响因子:
    5
  • 作者:
    Islam, Rumana;Cicek, Nazim;Levin, David
  • 通讯作者:
    Levin, David

Levin, David的其他文献

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{{ truncateString('Levin, David', 18)}}的其他基金

Simulation-Driven Graphics and Fabrication
仿真驱动的图形和制造
  • 批准号:
    CRC-2021-00227
  • 财政年份:
    2022
  • 资助金额:
    $ 2.04万
  • 项目类别:
    Canada Research Chairs
Process 11 Twin-Screw Extruder for Advanced Polymer Blending
用于高级聚合物共混的 Process 11 双螺杆挤出机
  • 批准号:
    RTI-2023-00228
  • 财政年份:
    2022
  • 资助金额:
    $ 2.04万
  • 项目类别:
    Research Tools and Instruments
Bioengineering Next Generation Biopolymers
生物工程下一代生物聚合物
  • 批准号:
    RGPIN-2017-04945
  • 财政年份:
    2021
  • 资助金额:
    $ 2.04万
  • 项目类别:
    Discovery Grants Program - Individual
Simulation-Driven Graphics And Fabrication
仿真驱动的图形和制造
  • 批准号:
    CRC-2016-00078
  • 财政年份:
    2021
  • 资助金额:
    $ 2.04万
  • 项目类别:
    Canada Research Chairs
Big Data for Fast and Accurate Numerical Simulation of Mechanical Structures
大数据用于快速准确的机械结构数值模拟
  • 批准号:
    RGPIN-2017-05524
  • 财政年份:
    2021
  • 资助金额:
    $ 2.04万
  • 项目类别:
    Discovery Grants Program - Individual
Simulation-Driven Graphics and Fabrication
仿真驱动的图形和制造
  • 批准号:
    CRC-2016-00078
  • 财政年份:
    2020
  • 资助金额:
    $ 2.04万
  • 项目类别:
    Canada Research Chairs
Big Data for Fast and Accurate Numerical Simulation of Mechanical Structures
大数据用于快速准确的机械结构数值模拟
  • 批准号:
    RGPIN-2017-05524
  • 财政年份:
    2020
  • 资助金额:
    $ 2.04万
  • 项目类别:
    Discovery Grants Program - Individual
Bioengineering Next Generation Biopolymers
生物工程下一代生物聚合物
  • 批准号:
    RGPIN-2017-04945
  • 财政年份:
    2020
  • 资助金额:
    $ 2.04万
  • 项目类别:
    Discovery Grants Program - Individual
Big Data for Fast and Accurate Numerical Simulation of Mechanical Structures
大数据用于快速准确的机械结构数值模拟
  • 批准号:
    RGPIN-2017-05524
  • 财政年份:
    2019
  • 资助金额:
    $ 2.04万
  • 项目类别:
    Discovery Grants Program - Individual
Simulation-Driven Graphics and Fabrication
仿真驱动的图形和制造
  • 批准号:
    CRC-2016-00078
  • 财政年份:
    2019
  • 资助金额:
    $ 2.04万
  • 项目类别:
    Canada Research Chairs

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