Physics-informed deep-learning applications to experimental fluid mechanics

arXiv:2203.15402v2 Announce Type: replace-cross
Abstract: High-resolution reconstruction of flow-field data from low-resolution and noisy measurements is of interest due to the prevalence of such problems in experimental fluid mechanics, where the measurement data are in general sparse, incomplete and noisy. Deep-learning approaches have been shown suitable for such super-resolution tasks. However, a high number of high-resolution examples is needed, which may not be available for many cases. Moreover, the obtained predictions may lack in complying with the physical principles, e.g. …

abstract applications arxiv cs.lg data experimental flow general low measurement physics physics.flu-dyn physics-informed tasks type