CAREER: A Parallel and Efficient Computational Framework for Unified Volumetric Meshing in Large-Scale 3D/4D Anisotropy

职业生涯：大规模 3D/4D 各向异性中统一体积网格划分的并行高效计算框架

基本信息

批准号：
1845962
负责人：
Zichun Zhong
金额：
$ 50万
依托单位：
Wayne State University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-03-15 至 2025-02-28
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1845962&HistoricalAwards=false
关键词：
CAREER Parallel Efficient Computational Framework

项目摘要

This proposal develops a computational framework that helps the domain scientists who employ advanced cyberinfrastructure ecosystem (e.g., for engineering, manufacturing, healthcare, etc.) to realistically and efficiently reconstruct, visualize, and analyze 3D and 4D (space-time) volumetric objects with complex geometric structures and highly anisotropic properties (such properties are characterized by the presence of specified orientations and aspect ratios in the system). For example, in mechanical engineering, it is necessary to interactively design and model mechanical parts with user-required high-quality measures and standards. The computational framework enables fabrication of such mechanical parts with specified microstructure that can be efficiently produced to sustain much stronger stress and strain compared with those without endowing such properties, which leads to significant impact on the next-generation mechanical component design. As an integral part of the PI's career development, the educational plan emphasizes on the integration of education and research in different aspects through the PI's new "3D hands-on" education philosophy for K-12, undergraduate and graduate students. This project thus serves the national interest, as stated by NSF's mission: to promote the progress of science; to advance the national health, prosperity and welfare. The research goal of this project focuses on a computational framework for anisotropic volumetric meshing, a foundational as well as translational research impacting a broad range of scientific domains. The capability and usability of the meshing framework are evaluated by investigating fabrication of objects with internal microstructures and construction of anisotropic volumetric models to capture the organ and tissue shape. This work has the following primary components: (1) Computing high-dimensional geometric embedding based on Nash theorem in parallel: the computational realization of high-dimensional geometric embedding makes modeling complex objects with multiple tensor features being built and solved in parallel in a large linear system. (2) Modeling multi-shape of mesh element in a unified particle framework: the particle system flexibly and effectively generates high-quality honeycomb, tetrahedral, and hexahedral (grid) patterns, which are exactly designed for meshing structure. The optimization procedure is easily formulated for parallelism in the high-dimensional space. (3) Generating 3D/4D anisotropic mesh in parallel: the final multi-shape anisotropic meshes are computed in parallel in the high-dimensional space with simple Euclidean computations under the isotropic metric. The primary outcome of this project is a 3D/4D-ParaAnisoMesh system.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该提案开发了一个计算框架，该框架可以帮助采用高级网络基础结构生态系统（例如，用于工程，制造，医疗保健等），实际上有效地重建，可视化和分析3D和4D（时空）的体积和高度的体质（相当）的特征（时代）的特征（时空），并分析3D和4D（时空）的特征（时代）的特征性属性（属性）。系统中的比率）。例如，在机械工程中，有必要使用用户享有的高质量措施和标准的交互式设计和建模机械零件。该计算框架可以与没有赋予此类特性相比，可以有效地生产具有指定的微观结构的机械零件，从而有效地产生更强的应力和应变，从而对下一代机械组件设计产生重大影响。作为PI职业发展不可或缺的一部分，教育计划强调了通过PI的新“ 3D动手动手”教育哲学，本科生和研究生的新“ 3D动手”教育理念。正如NSF的使命所指出的那样：促进科学的进步；促进国家健康，繁荣和福利。该项目的研究目标集中于各向异性体积网络的计算框架，这是一项影响广泛的科学领域的基础和翻译研究。通过研究具有内部微观结构的物体以及构建各向异性体积模型以捕获器官和组织形状的物体来评估网络框架的能力和可用性。这项工作具有以下主要组件：（1）基于NASH定理的高维几何嵌入：高维几何嵌入的计算实现使建模复杂的对象建模具有多个张量特征的复杂对象，并在大型线性系统中并行构建和求解。（2）在统一的粒子框架中对网格元素的多形进行建模：粒子系统灵活而有效地生成高质量的蜂窝，四面体和六面体（网格）图案，这些图案恰好是用于梅图结构的。在高维空间中，很容易为并行性制定优化过程。（3）并联生成3D/4D各向异性网格：最终的多形各向异性网络在高维空间中并联计算，并在各向同性度量下使用简单的欧几里得计算。该项目的主要结果是一个3D/4D-Paraanisomesh系统。该奖项反映了NSF的法定任务，并且使用基金会的知识分子优点和更广泛的影响评估标准，被认为值得通过评估来获得支持。

项目成果

期刊论文数量（15）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

JointVesselNet: Joint Volume-Projection Convolutional Embedding Networks for 3D Cerebrovascular Segmentation

DOI：
10.1007/978-3-030-59725-2_11
发表时间：
2020-10
期刊：
影响因子：
0
作者：
Yifan Wang-;Guoli Yan;Haikuan Zhu;S. Buch;Ying Wang;E. Haacke;Jing Hua;Z. Zhong
通讯作者：
Yifan Wang-;Guoli Yan;Haikuan Zhu;S. Buch;Ying Wang;E. Haacke;Jing Hua;Z. Zhong

VC-Net: Deep Volume-Composition Networks for Segmentation and Visualization of Highly Sparse and Noisy Image Data

DOI：
10.1109/tvcg.2020.3030374
发表时间：
2021-02-01
期刊：
IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
影响因子：
5.2
作者：
Wang, Yifan;Yan, Guoli;Zhong, Zichun
通讯作者：
Zhong, Zichun

A-CNN: Annularly Convolutional Neural Networks on Point Clouds

DOI：
10.1109/cvpr.2019.00760
发表时间：
2019-01-01
期刊：
2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2019)
影响因子：
0
作者：
Komarichev, Artem;Zhong, Zichun;Hua, Jing
通讯作者：
Hua, Jing

TCB-spline-based Image Vectorization