Unpaired Downscaling of Fluid Flows with Diffusion Bridges
This method addresses the need for flexible and computationally efficient downscaling in climate simulations, though it appears incremental as it adapts existing generative models to a specific domain.
The authors tackled the problem of downscaling geophysical fluid simulations without paired training data by using diffusion bridges, enabling the generation of high-resolution images from low-resolution inputs and allowing computation of statistics without additional calibration.
We present a method to downscale idealized geophysical fluid simulations using generative models based on diffusion maps. By analyzing the Fourier spectra of images drawn from different data distributions, we show how one can chain together two independent conditional diffusion models for use in domain translation. The resulting transformation is a diffusion bridge between a low resolution and a high resolution dataset and allows for new sample generation of high-resolution images given specific low resolution features. The ability to generate new samples allows for the computation of any statistic of interest, without any additional calibration or training. Our unsupervised setup is also designed to downscale images without access to paired training data; this flexibility allows for the combination of multiple source and target domains without additional training. We demonstrate that the method enhances resolution and corrects context-dependent biases in geophysical fluid simulations, including in extreme events. We anticipate that the same method can be used to downscale the output of climate simulations, including temperature and precipitation fields, without needing to train a new model for each application and providing a significant computational cost savings.