Machine learning for flow field measurements: a perspective

Advancements in machine-learning (ML) techniques are driving a paradigm shift in image processing. Flow diagnostics with optical techniques is not an exception. Considering the existing and foreseeable disruptive developments in flow field measurement techniques, we elaborate this perspective, particularly focused to the field of particle image velocimetry. The driving forces for the advancements in ML methods for flow field measurements in recent years are reviewed in terms of image preprocessing, data treatment and conditioning. Finally, possible routes for further developments are highlighted.

Automated summary

Advancements in machine-learning techniques are driving a paradigm shift in image processing, and optical techniques play an important role in flow diagnostics. This perspective reviews the recent advancements in machine learning methods for flow field measurements and highlights possible routes for further developments.