4 papers
Deep Learning-Based Statistical Downscaling of Sea Surface Temperature Using a Residual Corrective Neural Network
Onkar Jadhav, Tim French, Ivica Janekovic +2
The large-scale oceanic and atmospheric forecasts provided by global climate models typically lack sufficient resolution to accurately capture the response of the coastal ocean to…
Patch-PODiff-ViT: Structured Latent Diffusion with Patchwise POD for Super-Resolution and Uncertainty Quantification
Onkar Jadhav, Tim French, Matthew Rayson +1
Diffusion models enable probabilistic super-resolution and conditional generation, but pixel-space methods are computationally expensive and learned latent spaces often lack interp…
PODiff: Latent Diffusion in Proper Orthogonal Decomposition Space for Scientific Super-Resolution
Onkar Jadhav, Tim French, Matthew Rayson +1
Probabilistic super-resolution of high-dimensional spatial fields using diffusion models is often computationally prohibitive due to the cost of operating directly in pixel space.…
Exploring Model Misspecification in Statistical Finite Elements via Shallow Water Equations
Connor Duffin, Paul Branson, Matt Rayson +3
The abundance of observed data in recent years has increased the number of statistical augmentations to complex models across science and engineering. By augmentation we mean coher…