Fundamental physics or data science? Why not both: a data-driven modelling framework for interfacial microflows

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This exciting project lives at the interface between multi-physics modelling, high performance computing and data-driven approaches. The 21st century has brought a revolution in micromanufacturing techniques (LCD, 3D printing etc.) that require understanding and efficient deployment of knowledge at scales below those currently accessible. Enter data-driven equation discovery techniques: novel surrogate modelling methods which can provide insight in scenarios in which simulation or experimental data are available, but traditional derivation approaches break down. Our challenge is to create a new computational framework that harnesses the power of these approaches towards generating new meaningful understanding of fluid flows at small scales.The project will involve a productive interplay between mathematical modelling, asymptotic analysis, computational fluid dynamics and data-driven methods, as well as multi-physics elements and heterogeneous approaches more generally. Several useful resources are provided below, showcasing some of the capabilities of these techniques in related contexts, as well as providing a broader perspective into this rapidly evolving research area.