Physics-informed machine learning for reconstructing three-dimensional velocity fields from two-dimensional concentration fields
We present a multi-domain physics-informed neural network (MDPINN) framework for reconstructing three-dimensional (3D) velocity fields from sparse two-dimensional concentration measurements in complex microfluidic flows. The approach is demonstrated in intersecting channels at a Reynolds number of 320, where strong 3D...