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Authors: Daniel Kelshaw, Georgios Rigas, Luca Magri
Venue: Accepted to NeurIPS 2022 Workshop on Machine Learning and the Physical Sciences
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.
All code to reproduce experiments can be found in the ./src folder:
- Generate data using ./src/data/generate_kolmogorov.py
- Train model using ./src/experiments/base_experiment.py
- Run post-processing using ./src/postprocessing/generate_plot.py
Defaults have been set to the same as used in the paper.
@inproceedings{Kelshaw2022,
title = {Physics-Informed CNNs for Super-Resolution of Sparse Observations on Dynamical Systems},
author = {Daniel Kelshaw and Georgios Rigas and Luca Magri},
booktitle = {NeurIPS 2022 Workshop on Machine Learning and the Physical Sciences},
year = {2022},
url = {https://arxiv.org/abs/2207.00556},
}