CAREER: Simulation of Geometrically Flexible Materials with Applications to Computer Graphics and Computational Science

职业：几何柔性材料的模拟及其在计算机图形学和计算科学中的应用

• 批准号: 2153851
• 负责人: Chenfanfu Jiang
• 金额: $ 52.43万
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2153851&HistoricalAwards=false
• 关键词: CAREER; Simulation; Geometrically; Flexible; Materials

High-fidelity physics-based simulation of 3D materials and natural phenomena has become essential in many computational science domains such as structural engineering and vehicle/aircraft design, as well as a critical component in motion pictures, visual effects (VFX), animation, and video games. Numerical simulations have further found an increasing breadth of new applications such as real-time VFX previews, virtual reality games, interactive surgical training, predictive soft robotics, and computational fabrication. While theoretical computation capacity is now less of an impediment, a timely opportunity emerges for innovations in designing new numerical algorithms that mathematically resolve complex geometry and multi-physics with high accuracy and can best utilize new computational platforms with plausible scalability. While contributing towards this direction, the project will also directly promote modern interdisciplinary studies and education in scientific computing, mechanical engineering, and human-robot interaction. The application to simulating virtual humans will enable clinical training software, which not only improves patient care but also eliminates animal experiments. The support for large-scale geophysical simulation saves lives by improving the prediction of disasters like avalanches and landslides. The innovation of a versatile multi-physics system facilitates advances in climate sciences by modeling Arctic sea ice. This project will produce highly useful software systems for non-simulation experts and educational tools for STEM students. It will also strongly encourage the involvement of undergraduate students, underrepresented minorities, and women through a versatile set of educational events, exchange programs, and outreach activities.This project will develop innovative computational algorithms, including flexible treatment of thin structures with the Material Point Method, a unified multi-material multi-physics framework to capture versatile phenomena, along with novel approaches harnessing the power of next-generation multi-GPU platforms. Co-dimensional geometries (metallic shells, fluid sheets, filaments, biological membranes, fibrous composites, threaded alloys, etc.) will be a primary focus. The project will build innovative geometric representations that are robust for heterogeneous materials, and numerical algorithms that naturally capture multi-physics. The innovative treatment of thin structures will enable new applications such as fiber-level wood crack prediction and fibrous food design/processing. The unified framework will create an exciting opportunity to improve clinical planning and training by enabling high-fidelity biomechanical simulation directly from tomographic imaging, while investigations into numerical stability and computational scalability will advance synergistic domains in computer graphics and computational science at large.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.

在许多计算科学领域（例如结构工程和车辆/飞机设计）以及电影，视觉效果（VFX），动画和视频游戏等许多计算科学领域中，基于3D材料和自然现象的高保真模拟已成为必不可少的。 数值模拟进一步发现了越来越多的新应用程序，例如实时VFX预览，虚拟现实游戏，交互式手术训练，预测性软机器人技术和计算制造。虽然现在的理论计算能力不再是一种障碍，但在设计新的数值算法时，创新出现了及时的机会，该算法可以数学上以高精度来解决复杂的几何形状和多物理学，并且可以最好地利用具有可伸缩性的新计算平台。在朝着这一方向做出贡献的同时，该项目还将直接促进现代的跨学科研究和科学计算，机械工程和人类机器人互动中的教育。模拟虚拟人类的应用将启用临床培训软件，这不仅可以改善患者护理，还可以消除动物实验。对大规模地球物理模拟的支持通过改善雪崩和滑坡等灾难的预测来挽救生命。多功能多物理系统的创新通过对北极海冰进行建模，促进了气候科学的进步。该项目将为STEM学生提供非仿真专家和教育工具的非常有用的软件系统。它还将强烈鼓励本科生，代表性不足的少数群体以及女性通过多功能的教育活动，交流计划和外展活动的参与。该项目将开发创新的计算算法，包括对薄结构的灵活处理，包括具有物质点方法的薄结构，统一的多物质多物质框架，以捕获多种物质的框架，以及捕获多功能的框架，以及捕获多功能的框架。多GPU平台。共同维数（金属壳，流体板，细丝，生物膜，纤维复合材料，螺纹合金等）将是主要重点。该项目将建立创新的几何表示，对异质材料和自然捕获多物理学的数值算法具有鲁棒性。薄结构的创新处理将使新应用，例如纤维水平的木裂裂纹预测和纤维化的食物设计/加工。统一的框架将创造一个令人兴奋的机会，通过直接从断层扫描成像中启用高保真生物力学模拟来改善临床计划和培训，而对数值稳定性和计算可伸缩性的调查将推动计算机图形和计算科学中的协同域名，这是NSF的法定任务，反映了综述的范围，并通过评估了基础，这一范围又反映了范围。

项目成果

Second-order Stencil Descent for Interior-point Hyperelasticity

• DOI: 10.1145/3592104
• 发表时间: 2023-07
• 期刊: ACM Transactions on Graphics (TOG)
• 作者: L. Lan;Minchen Li;Chenfanfu Jiang;Huamin Wang;Yin Yang

A novel discretization and numerical solver for non-fourier diffusion

• DOI: 10.1145/3414685.3417863
• 发表时间: 2020-11
• 期刊: ACM Transactions on Graphics (TOG)
• 作者: Tao Xue;Haozhe Su;Chengguizi Han;Chenfanfu Jiang;Mridul Aanjaneya

Penetration-free projective dynamics on the GPU

• DOI: 10.1145/3528223.3530069
• 发表时间: 2022-07
• 期刊: ACM Transactions on Graphics (TOG)
• 作者: L. Lan;Guanqun Ma;Yin Yang;Changxi Zheng;Minchen Li;Chenfanfu Jiang

HoD-Net: High-Order Differentiable Deep Neural Networks and Applications

• DOI: 10.1609/aaai.v36i8.20799
• 发表时间: 2022-06
• 作者: Siyuan Shen;Tianjia Shao;Kun Zhou;Chenfanfu Jiang;Feng Luo;Yin Yang

A Sparse Distributed Gigascale Resolution Material Point Method

• DOI: 10.1145/3570160
• 发表时间: 2022-11
• 期刊: ACM Transactions on Graphics
• 影响因子: 6.2
• 作者: Yuxing Qiu;S. Reeve;Minchen Li;Yin Yang;S. Slattery;Chenfanfu Jiang