The pandemic of 2019 has led to an enormous interest in all aspects of modeling and simulation of infectious diseases. One central issue is the redesign and deployment of ventilation systems to mitigate the transmission of infectious diseases, produced by respiratory emissions such as coughs. This work seeks to develop a combined Digital-Twin and Machine-Learning framework to optimize ventilation systems by building on rapidly computable respiratory emission models developed in Zohdi (Comput Mech 64:1025–1034, 2020). This framework ascertains the placement and flow rates of multiple ventilation units, in order to optimally sequester particles released from respiratory emissions such as coughs, sneezes, etc. Numerical examples are provided to illustrate the framework.

2019 年的大流行引发了人们对传染病建模和模拟各个方面的巨大兴趣。其中一个核心问题是重新设计和部署通风系统，以减轻咳嗽等呼吸道排放物产生的传染病的传播。这项工作旨在开发一个组合的数字孪生和机器学习框架，通过构建 Zohdi 开发的快速计算呼吸排放模型来优化通风系统（Comput Mech 64:1025–1034, 2020）。该框架确定了多个通风装置的位置和流量，以便最佳地隔离咳嗽、打喷嚏等呼吸排放物释放的颗粒。提供了数值示例来说明该框架。