CDS&E: AI-RHEO: Learning coarse-graining of complex fluids

CDS

• 批准号: 2204226
• 负责人: George Biros
• 金额: $ 40.53万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2204226&HistoricalAwards=false
• 关键词: CDS& AI; RHEO; Learning; coarse

Stokesian complex fluid flows describe transport phenomena at small scales. Examples include flows inside biological cells, blood flow in capillaries and microfluidic devices, DNA hydrodynamics, lab-on-a-chip industrial and medical devices, bacterial flows, and polymer flows. Understanding and predicting the behavior of Stokesian complex fluids using numerical simulations is fundamental in understanding mechano-biological mechanisms, design of microfluidic devices, medical robotics, and many other applications. Complex fluid flows are challenging to simulate because they involve solid-fluid interaction, moving interfaces and complex geometries, non-local and multiscale couplings in both space and time, and highly nonlinear and often chaotic dynamics. This project focuses on the development of methods that will significantly advance the state-of-the-art of complex fluid simulation technologies. The specific goals of this project include the following. (A) The design of high-performance computing (HPC) algorithms that integrate dimension reduction and deep learning methods with integral equation methods and result in orders-of-magnitude speedups of predictive simulations of Stokesian complex fluid flows. (B) The design and deployment of HPC software infrastructure that automates configuration sampling, operator splitting, deep network training, and inference for a large class of complex fluids. (C) Evaluation of the proposed methodology on three problems: calculation of effective properties, parameter estimation, and shape optimization of microfluidic devices. The complex fluid solvers developed in this project, will impact a broad spectrum of disciplines in sciences and engineering that involve problems with moving interfaces and microstructure evolution. Furthermore, the project aims to engage a number of graduate and undergraduate students at the interface of HPC, machine and deep learning, integral equations, and complex fluids.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.

斯托克斯复杂流体流动描述了小尺度下的输运现象。例子包括生物细胞内的流动、毛细管和微流体装置中的血流、DNA流体动力学、芯片实验室工业和医疗装置、细菌流动和聚合物流动。使用数值模拟来理解和预测斯托克斯复杂流体的行为对于理解机械生物学机制、微流体设备设计、医疗机器人和许多其他应用是至关重要的。复杂的流体流动是具有挑战性的模拟，因为它们涉及固体-流体相互作用，移动界面和复杂的几何形状，在空间和时间上的非局部和多尺度耦合，以及高度非线性和经常混沌动力学。该项目的重点是开发将显着推进复杂流体模拟技术的最新发展的方法。该项目的具体目标如下。(A)设计高性能计算（HPC）算法，将降维和深度学习方法与积分方程方法相结合，实现斯托克斯复杂流体流动预测模拟的数量级加速。(B)HPC软件基础设施的设计和部署，可自动化配置采样、操作员拆分、深度网络训练和大量复杂流体的推理。 (C)评价所提出的方法对三个问题：计算的有效性能，参数估计，和形状优化的微流控器件。 该项目开发的复杂流体求解器将影响科学和工程领域的广泛学科，这些学科涉及移动界面和微观结构演化问题。 此外，该项目旨在吸引大量研究生和本科生参与HPC、机器和深度学习、积分方程和复杂流体的接口。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

GrainNN: A neighbor-aware long short-term memory network for predicting microstructure evolution during polycrystalline grain formation

• DOI: 10.1016/j.commatsci.2022.111927
• 发表时间: 2023-02
• 期刊: Computational Materials Science
• 影响因子: 3.3
• 作者: Yigong Qin;S. DeWitt;B. Radhakrishnan;G. Biros