Geometry-free prediction of inertial lift forces in microfluidic devices using deep learning

ArXi:2605.08109v1 Announce Type: new Inertial microfluidic devices (IMDs) offer low-cost, high-throughput alternative techniques for many traditional particle- (or cell-) manipulation tasks, but simulating them requires being able to predict particle migration, and thus particle lift forces, under a variety of possible channel geometries. Recent work has nstrated that machine learning models can be used to drastically speed up these numerical simulations, but doing so required