FSE IMPACT Fund (Swansea University)

This project aims to develop a physics-data combined machine learning method for reduced-order modelling of urban-scale turbulent airflows. The developed method can seamlessly integrate the governing partial differential equations and the available data into feed-forward neural networks. Compared to traditional data-driven ROM methods, this method can greatly improve the prediction accuracy and generalization capacity of reduced-order models.