RAPID: Collaborative Research: Modeling, Analysis and Control of COVID-19 Spread in an Aircraft Cabin using Physics Informed Deep Learning

RAPID：协作研究：使用物理信息深度学习对机舱内的 COVID-19 传播进行建模、分析和控制

• 批准号: 2031029
• 负责人: Padmanabhan Seshaiyer
• 金额: $ 6万
• 依托单位: George Mason University
• 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: 2020-06-01 至 2023-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2031029&HistoricalAwards=false
• 关键词: RAPID; Collaborative; Research; Modeling; Analysis

This project will model, analyze, predict, and present control mechanisms for a COVID-19 outbreak through an airborne infection in an aircraft cabin. As air travel resumes, it is expected that many passengers would be exposed to and possibly infected by the COVID-19 virus. As a result, there is an urgent need to rapidly develop solutions to determine the speed of the contagion by understanding the dynamics of the airflow inside aircraft. This research will combine four separate multi-physics models representing fluid dynamics, scalar transport, epidemiology, and airborne infection to analyze the spread of COVID-19 within a closed system such as an airplane. The multidisciplinary nature of this research will yield new algorithms at the interface of computational mathematics, deep learning, data science, epidemiology, and fluid dynamics and will provide novel techniques that can be directly applied to large-scale data to allow efficient and powerful data analysis. The project will also serve as valuable training for students. Open-source codes will be made available to the user community and will be open to contributions from end-users, academic researchers, industry members, practitioners, and government research labs. The research may also be extended to other physical spaces, such as marine vessels, trains, buses, or any other medium of public transportation systems.This research will accomplish the following specific objectives (a) develop a fully 3-dimensional computational model capturing realistic geometry and coupling four different physical and biological systems; (b) implement a hidden multi-physics neural network framework to enable data assimilation and; (c) evaluate the predictive capability using simulated, experimental and observational data in addition to developing and studying novel control and reinforcement learning mechanisms. The framework considers the characteristics of the exhalation of the droplets from COVID-19 infected members on an airplane that may not be wearing face masks, tracking the dispersion of these droplets, and tracking the inhalation of the droplets by susceptible passengers through these coupled multi-physics models. The research will help to develop a novel physics-informed deep-learning framework that will be capable of encoding the multi-physics system of equations modeled into the neural networks while being agnostic to the geometry or the initial and boundary conditions. Progress on the goals will provide advances in data-driven discovery, which will allow a better understanding of the impact of COVID-19. This grant is being awarded using funds made available by the Coronavirus Aid, Relief, and Economic Security (CARES) Act supplement allocated to MPS.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.

该项目将通过飞机机舱内的空气传播感染对COVID-19爆发进行建模、分析、预测和提出控制机制。随着航空旅行的恢复，预计许多乘客将暴露于COVID-19病毒并可能受到感染。因此，迫切需要快速开发解决方案，通过了解飞机内气流的动力学来确定传染速度。这项研究将结合联合收割机四个独立的多物理模型，分别代表流体动力学、标量传输、流行病学和空气传播感染，以分析COVID-19在飞机等封闭系统内的传播。这项研究的多学科性质将在计算数学、深度学习、数据科学、流行病学和流体动力学的界面上产生新的算法，并将提供可直接应用于大规模数据的新技术，以实现高效和强大的数据分析。该项目也将成为学生的宝贵培训。开源代码将提供给用户社区，并将开放给最终用户，学术研究人员，行业成员，从业人员和政府研究实验室的贡献。这项研究还可以扩展到其他物理空间，如船舶、火车、公共汽车或公共交通系统的任何其他媒介。这项研究将实现以下具体目标：（a）开发一个完全三维的计算模型，捕捉真实的几何形状，并耦合四个不同的物理和生物系统;（B）实现一个隐藏的多物理神经网络框架，以实现数据同化;（c）除了开发和研究新的控制和强化学习机制外，还利用模拟、实验和观察数据评估预测能力。该框架考虑了飞机上可能未戴口罩的COVID-19感染成员呼出的液滴的特征，跟踪这些液滴的分散，并通过这些耦合的多物理模型跟踪易感乘客吸入的液滴。这项研究将有助于开发一种新的物理深度学习框架，该框架能够将多物理方程系统编码到神经网络中，同时对几何形状或初始和边界条件不可知。这些目标的进展将推动数据驱动的发现，从而更好地了解COVID-19的影响。该补助金是使用分配给MPS的冠状病毒援助，救济和经济安全（CARES）法案补充提供的资金授予的。该奖项反映了NSF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。

项目成果

Computational modeling, analysis and simulation for lockdown dynamics of COVID-19 and domestic violence

COVID-19 和家庭暴力的封锁动态的计算建模、分析和模拟

• 发表时间: 2022
• 期刊: Electronic research archive
• 影响因子: 0.8
• 作者: Comfort Ohajunwa, Carmen Caiseda

Mathematical modeling, analysis, and simulation of the COVID-19 pandemic with explicit and implicit behavioral changes

对具有显性和隐性行为变化的 COVID-19 大流行进行数学建模、分析和模拟

• 发表时间: 2020
• 期刊: Computational and Mathematical Biophysics
• 作者: Ohajunwa, Comfort;Kumar, Kirthi;Seshaiyer, Padmanabhan
• 通讯作者: Seshaiyer, Padmanabhan

Efficient Physics Informed Neural Networks Coupled with Domain Decomposition Methods for Solving Coupled Multi-physics Problems

高效的物理信息神经网络与域分解方法相结合，用于解决耦合多物理问题

• 发表时间: 2022
• 期刊: Lecture notes in mechanical engineering
• 作者: Long Nguyen, Maziar Raissi

Mathematical Modeling, Analysis, and Simulation of the COVID-19 Pandemic with Behavioral Patterns and Group Mixing

• 发表时间: 2021
• 作者: Comfort Ohajunwa;P. Seshaiyer