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
• 负责人: Yin Yang
• 金额: $ 43.27万
• 依托单位: University of New Mexico
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2020-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1717972&HistoricalAwards=false
• 关键词: CHS; Small; Towards; Next; Generation

Using a digital computer to accurately simulate soft objects that deform under external interactions is a fundamental problem in a wide range of scientific and engineering fields. For example, without being able to deliver a faithful force-displacement response, virtual surgical training is hardly effective and provides users with misleading experiences. In the past decade, the number of simulation degrees of freedom (DOFs) for deformable models has increased from hundreds to hundreds-of-thousands and even millions. Computing hardware that has become more and more powerful has contributed significantly to this development, but unfortunately it is unlikely that in the future computer simulation will continue to benefit dramatically from increased processor frequency. Indeed, in the last few years the chip industry has already moved the emphasis from a faster processor clock to multi-core architectures. On the other hand, with the widespread adoption of advanced acquisition devices/techniques, the complexity and scale of the data that can be handled by computers have grown exponentially, and large-scale geometries are becoming ubiquitous in modern 3D data processing. This new era of data explosion imposes unprecedented challenges on deformable simulation. Existing methods typically use one-stop solvers that calculate all the unknown DOFs of a system, but that is computationally intensive due to the underlying high-dimensional numerical integration. Even with state-of-the-art hardware, deformable simulation can still take hours, days, or even weeks for massive scenarios. Clearly, conventional simulation methodologies fail to well accommodate distributed computing resource allocation, and become more and more cumbersome with bigger and bigger datasets. This calls for rebranded algorithmic frameworks and dedicated numerical procedures for large-scale geometrically-complex and nonlinear deformable models that empower next-generation graphics applications. Motivated by these grand challenges, this project systematically investigates a collection of theoretical advancements, computational techniques, and numerical implementations that push the frontier of large-scale nonlinear deformable models to "post Moore's law." Specifically, the intellectual merit of the research will comprise the following aspects:o The project will devise a theoretically grounded domain decomposition based parallel deformable simulator. By weakening inter-domain linkages, the domain-level computations become independent and parallelizable. The coupling mechanism will be generalized and enriched so that non-conforming and overlapping domain decompositions are made possible. This includes an in-depth optimization of the domain tessellation under specified hardware configurations. Simulation reusability will be further enhanced through a novel technique called cellular domains.o The project will deepen the current understanding of large-scale model reduction and re-forge this useful tool in the context of parallel computing. In particular, how to utilize power iteration to obtain the spectral analysis will be explored. Furthermore, geometry-based reduction directly dictates reduced DOFs and has a more robust simulation even under imposed extreme constraints.o A well-argued computational theory is less practicable unless encapsulated by a set of carefully engineered implementations. Accordingly, the project will also design customized numerical procedures paired with proposed algorithmic techniques, and the entire simulation framework will be fine-tuned at the system level, solver level, and sub-solver level by leveraging unique data patterns, numerical behaviors, and problem structures of domain decomposed deformable models.o As a testbed platform, the project will develop a novel real-time human tongue motion visualization system. Over 8% of U.S. children have a communication or swallowing disorder. Built upon the new deformation solver, an ultrasound-imaging-driven real-time human tongue visualization system will be developed to assist doctors and speech therapists to better understand the pathological mechanism behind this disease and plan more effective subject-specific medical/physical treatments.

使用数字计算机准确模拟在外部互动下变形的软体物体是在广泛的科学和工程领域中的一个基本问题。 例如，在不能够提供忠实的力量响应的情况下，虚拟手术训练几乎无效，并为用户提供了误导性的经验。 在过去的十年中，可变形模型的模拟自由度（DOF）的数量已从数十万甚至数百万增加。 计算越来越强大的硬件为这一开发做出了重大贡献，但是不幸的是，在将来的计算机模拟中，不太可能继续从增加的处理器频率中受益匪浅。 确实，在过去的几年中，芯片行业已经将重点从更快的处理器时钟转移到了多核架构。 另一方面，随着高级采集设备/技术的广泛采用，计算机可以处理的数据的复杂性和规模呈指数增长，并且在现代3D数据处理中，大规模的几何形状变得无处不在。 这个数据爆炸的新时代对可变形模拟施加了前所未有的挑战。 现有方法通常使用一站式求解器来计算系统的所有未知DOF，但由于基本的高维数值集成，因此在计算上是计算密集的。 即使使用最先进的硬件，可变形的模拟仍可能需要数小时，天甚至几周才能进行大规模场景。 显然，传统的仿真方法无法很好地适应分布式计算资源分配，并且在越来越大的数据集的情况下变得越来越繁琐。 这要求重新命名算法框架和用于大规模的几何复合和非线性变形模型的专用数值程序，以增强下一代图形应用程序。 在这些巨大的挑战的推动下，该项目系统地研究了一系列理论进步，计算技术和数值实现，这些实现将大规模非线性可变形模型的边界推向了“摩尔后定律”。 具体而言，研究的智力优点将包括以下方面：o项目将基于理论上基于域的平行变形模拟器设计一个理论上扎根的域分解。 通过削弱域间链接，域级计算变得独立且可行。 耦合机制将被概括和富集，以便使不合格和重叠的域分解成为可能。 这包括在指定的硬件配置下对域Tessellation的深入优化。 通过一种称为蜂窝域的新技术，将进一步增强模拟可重复使用性。该项目将加深对大规模模型降低的理解，并在并行计算的背景下重新使用该有用的工具。 特别是，将探索如何利用幂迭代获得光谱分析。 此外，基于几何的还原直接决定减少的DOF，即使在强加的极端约束下也具有更强的模拟。除非由一组精心设计的实现封装，否则良好的计算理论是不可行的。 因此，该项目还将设计自定义的数值程序与提议的算法技术配对，整个仿真框架将通过利用独特的数据模式，数值行为，数值行为和域的问题结构来实现人工，将在系统级别上进行调查，并在系统水平，求解器级别和子解决方案上进行微调。 超过8％的美国儿童患有通讯或吞咽障碍。 将建立在新的变形求解器上，将开发出超声导致的实时人舌视觉化系统，以帮助医生和言语治疗师更好地了解这种疾病背后的病理机制，并计划更有效的主体特定于学科的医学/身体治疗。

项目成果

Medial Elastics: Efficient and Collision-Ready Deformation via Medial Axis Transform

内侧弹性：通过内侧轴变换实现高效且防碰撞的变形

• DOI: 10.1145/3384515
• 发表时间: 2020
• 期刊: ACM TRANSACTIONS ON GRAPHICS
• 影响因子: 6.2
• 作者: Lan Lei;Luo Ran;Fratarcangeli Marco;Xu Weiwei;Wang Huamin;Guo Xiaohu;Yao Junfeng;Yang Yin
• 通讯作者: Yang Yin

NNWarp: Neural Network-Based Nonlinear Deformation

NNWarp：基于神经网络的非线性变形

• DOI: 10.1109/tvcg.2018.2881451
• 发表时间: 2020-04-01
• 期刊: IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
• 影响因子: 5.2
• 作者: Luo, Ran;Shao, Tianjia;Yang, Yin
• 通讯作者: Yang, Yin