Physics-constrained adaptive learning for multi-physics optimization: Time-accurate prediction of chaotic and turbulent flows

The ability of fluid mechanics modelling to predict the evolution of a flow is enabled both by physical principles and empirical approaches. On the one hand, physical principles (for example conservation laws) are extrapolative - they provide predictions on phenomena that have not been observed. On the other hand, empirical modelling provides correlation functions within data. Artificial intelligence and machine learning are excellent at empirical modelling. In this project, the student will combine physical principles and empirical modelling into a unified approach: physics-constrained data-driven methods for multi-physics optimisation. The objectives are to constrain the governing equations of turbulence in machine learning. This will enable more accuracy, robustness, and generalization.