Physics-Informed CNNs for Super-Resolution of Sparse Observations on Dynamical Systems
This addresses the problem of reconstructing high-resolution data from sparse samples in experimental settings, with incremental improvements in turbulence modeling.
The paper tackles super-resolution of sparse observations on dynamical systems using physics-informed CNNs, showing improved reconstruction of finer turbulence scales in chaotic-turbulent Kolmogorov flow compared to classic interpolation methods.
In the absence of high-resolution samples, super-resolution of sparse observations on dynamical systems is a challenging problem with wide-reaching applications in experimental settings. We showcase the application of physics-informed convolutional neural networks for super-resolution of sparse observations on grids. Results are shown for the chaotic-turbulent Kolmogorov flow, demonstrating the potential of this method for resolving finer scales of turbulence when compared with classic interpolation methods, and thus effectively reconstructing missing physics.