• Proposes HADF, a hash-adaptive dynamic fusion implicit network that reconstructs high-resolution turbulent flows from low-resolution inputs, addressing paired-data scarcity and multi-scale requirements.
• Introduces a low-resolution consistency loss enabling training with partially unpaired datasets, eliminating the need for perfectly matched LR/HR pairs.
• Employs hash-adaptive spatial encoding and dynamic feature fusion with implicit neural representations, allowing arbitrary-resolution reconstruction in a unified framework.
• Demonstrates superior global and local reconstruction accuracy, robustness to noise, and one-time training for diverse resolutions, significantly reducing computational costs of turbulence data acquisition.