Deep recurrent optical flow learning for particle image velocimetry data

Christian Lagemann, Kai Lagemann, Sach Mukherjee, Wolfgang Schröder

Abstract: Particle image velocimetry is an imaging technique to determine the velocity components of flow fields, of use in a range of complex engineering problems including in environmental, aerospace and biomedical engineering. A recurrent neural network-based approach for learning displacement fields in an end-to-end manner is applied to this technique and achieves state-of-the-art accuracy and, moreover, allows generalization to new data, eliminating the need for traditional handcrafted models.