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

斯托克复杂流体流动描述了小尺度上的输运现象。例子包括生物细胞内部的流动，毛细血管和微流体装置中的血液流动，DNA流体动力学，芯片实验室工业和医疗设备，细菌流动和聚合物流动。利用数值模拟来理解和预测斯托克复杂流体的行为是理解机械生物学机制、设计微流体装置、医疗机器人和许多其他应用的基础。复杂流体流动的模拟具有挑战性，因为它们涉及固流相互作用、移动界面和复杂的几何形状、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是混沌的动力学。该项目侧重于开发能够显著推进复杂流体模拟技术发展的方法。该项目的具体目标包括以下内容。(A)高性能计算（HPC）算法的设计，该算法将降维和深度学习方法与积分方程方法相结合，使Stokesian复杂流体流动的预测模拟速度提高了数量级。(B) HPC软件基础设施的设计和部署，该基础设施可自动进行配置采样、操作员拆分、深度网络训练和大型复杂流体的推理。(C)针对微流控器件有效性能计算、参数估计和形状优化三个问题对提出的方法进行评价。该项目开发的复杂流体求解器将广泛影响涉及移动界面和微观结构演化问题的科学和工程学科。此外，该项目旨在吸引一些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界面。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.