Abstract
Inertial microfluidics allows for passive, label-free manipulation of particles suspended in a fluid. Physical experiments can understand the underlying mechanisms to an extent whereby inertial microfluidic devices are used in real-world applications such as disease diagnostics. However, design processes are often iterative and device optimisation can be improved. Numerical modelling has complementary capabilities to physical experiments, with access to full flow field data and control of design parameters. Numerical modelling is used to uncover the fundamental mechanisms in inertial microfluidics and provides evidence for physical experiments. In recent years, numerical modelling has been increasingly coupled to machine learning algorithms to uncover additional physics and provide fast solutions. In this perspective, I discuss the role numerical modelling will play in future inertial microfluidic device research and the opportunities to combine numerical modelling with machine learning algorithms. Two key areas for future research applying machine learning are highlighted; fast predictions of flow fields and the optimisation of design parameters. Developments in these areas would significantly reduce the resources required in device design and have the potential to uncover new applications.