A review of deep learning for super-resolution in fluid flows

Integrating deep learning with fluid dynamics presents a promising path for advancing the comprehension of complex flow phenomena within both theoretical and practical engineering domains. Despite this potential, considerable challenges persist, particularly regarding the calibration and training of deep learning models. This paper conducts an extensive review and analysis of recent developments in deep learning architectures that aim to enhance the accuracy of fluid flow data interpretation. It investigates various applications, architectural designs, and performance evaluation metrics. The analysis covers several models, including convolutional neural networks, generative adversarial networks, physics-informed neural networks, transformer models, diffusion models, and reinforcement learning frameworks, emphasizing components improving reconstruction capabilities. Standard performance metrics are employed to rigorously evaluate the models' reliability and efficacy in producing high-performance results applicable across spatiotemporal flow data. The findings emphasize the essential role of deep learning in representing fluid flows and address ongoing challenges related to the systems' high degrees of freedom, precision demands, and resilience to error.