Physics-Constrained Deep Learning for Surrogate Modeling of Dynamics of Fluids and Fluid-Structure Interaction

用于流体动力学和流固耦合代理建模的物理约束深度学习

基本信息

批准号：
1934300
负责人：
Jian-Xun Wang
金额：
$ 30.03万
依托单位：
University of Notre Dame
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-01-01 至 2022-12-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1934300&HistoricalAwards=false
关键词：
Physics Constrained Deep Learning Surrogate

项目摘要

This project will develop an innovative modeling framework for coupled dynamics of fluid-structure systems based on deep learning, which will contribute new knowledge on rapid modeling of dynamic systems in general. Complex fluids and their interactions with surrounding structures are ubiquitous in natural and industrial processes, e.g., blood flows in compliant vessels, flapping-wing miniature air vehicles, flexible risers in the offshore industry. Predictive modeling of fluid-structure interaction problems is of great significance in numerous engineering applications. However, existing models are primarily based on first-principles methods and numerical discretization techniques, which are computationally expensive and require significant domain expertise. This drawback poses great challenges to real-time predictions (e.g., clinical diagnosis of vascular diseases) and many-query applications (e.g., optimization design of aircraft and uncertainty quantification in high-consequence systems). This grant will support fundamental research on the development of a novel modeling framework by leveraging recent advances in machine learning and prior knowledge of physical principles. This new approach will enable rapid modeling and fast prediction for dynamics of fluid-structure systems, which will have strong practical impacts on a broad spectrum of real-world problems, including cardiovascular diagnosis, aerodynamic design, and active flow control. Therefore, the results of this research can help enhance U.S. healthcare/wellness, national security, and economic competitiveness. Moreover, the multi-disciplinary research topics across physical modeling and artificial intelligence can stimulate interest in the STEM disciplines among young people and thus will have a positive impact on science and engineering education.Data-based surrogate modeling is a computationally feasible way to tackle fluid-structure interaction problems that require rapid predictions or repeated model evaluations. Deep learning is becoming a popular surrogate modeling approach due to its capability of handling strong nonlinearity and high dimensionality. However, current success of deep learning in the computer science community heavily relies on large-scale labeled data, which are usually not available in the physical modeling community. To address this challenge, this research aims to pioneer a physics-constrained deep learning framework for surrogate modeling of fluid-structure interaction dynamics, which will enable efficient learning with sparse training data. Specifically, a structured deep neural network will be devised to encode the initial and boundary conditions, and the governing equations will be imposed during the training by redesigning the loss (or likelihood) functions to conform to the physics. Numerical experiments of a suite of dynamic fluid-structure interaction problems are planned to answer questions regarding the effect of adding physical constraints in deep learning and their potential in modeling complex physical systems in a parametric setting. This project tackles long-standing difficulties in surrogate modeling of complex dynamical systems with nonlinearity, high-dimensionality, and data scarcity, and contributes to the modeling of dynamical systems in general. The learning framework will bring revolutionary impacts on data-driven surrogate modeling by shifting the paradigm from black-box, data-intensive learning to physics-constrained, data-scarce learning.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.

该项目将开发一个创新的建模框架，以基于深度学习的流体结构系统的耦合动力学，这将在整个动态系统的快速建模上有助于新知识。复杂的液体及其与周围结构的相互作用在自然和工业过程中无处不在，例如，兼容的船只中的血液流动，吹翼的微型空中汽车，离岸行业的柔性立管。流体结构相互作用问题的预测建模在众多工程应用中具有重要意义。但是，现有模型主要基于第一原理方法和数值离散技术，这些技术在计算上昂贵，需要重要的领域专业知识。该缺点对实时预测（例如，血管疾病的临床诊断）和多质量应用（例如，飞机的优化设计和高分后果系统中的不确定性定量）面临着巨大的挑战。该赠款将通过利用机器学习的最新进展和物理原理知识的最新进展来支持有关开发新型建模框架的基础研究。这种新方法将对流体结构系统的动态进行快速建模和快速预测，这将对多种现实世界中的问题产生强大的实际影响，包括心血管诊断，空气动力学设计和主动流动控制。因此，这项研究的结果可以帮助增强美国的医疗保健/健康，国家安全和经济竞争力。此外，跨物理建模和人工智能的多学科研究主题可以刺激年轻人对STEM学科的兴趣，因此将对科学和工程教育产生积极的影响。基于DATA的替代模型是一种计算上可行的方式，可以解决需要快速预测或重复模型评估的流体结构问题。由于其能力处理强大的非线性和高维度，深度学习正在成为一种流行的替代建模方法。但是，计算机科学界的深度学习的当前成功在很大程度上依赖于大规模标记的数据，这些数据通常在物理建模社区中不可用。为了应对这一挑战，这项研究旨在为替代流体结构互动动态建模的物理限制的深度学习框架开拓，这将通过稀疏的培训数据实现有效的学习。具体而言，将设计一个结构化的深神经网络来编码初始条件和边界条件，并且在训练过程中将通过重新设计损失（或可能性）功能来构成训练期间，以符合物理学。一套动态流体结构相互作用问题的数值实验计划回答有关在参数设置中添加物理约束及其在对复杂物理系统建模的潜力中添加物理约束的效果的问题。该项目解决了具有非线性，高维性和数据稀缺性的复杂动力学系统的替代建模方面的长期困难，并有助于一般动态系统的建模。该学习框架将通过将范式从黑框，数据密集型学习转移到物理受限的，数据筛选学习的范式来对数据驱动的替代建模产生革命性的影响。该奖项反映了NSF的法定任务，并被认为是通过基金会的智力功能和广泛影响的评估来评估CRITERIA的评估。