13 papers
CRPS-LAM: Probabilistic Regional Weather Forecasting with Continuous Ranked Probability Score
Erik Larsson, Joel Oskarsson, Tomas Landelius +1
Limited-Area Models (LAMs) enable weather forecasting over regional domains at higher resolutions than what is computationally feasible for global models. At such high resolutions,…
Njord: A Probabilistic Graph Neural Network for Ensemble Ocean Forecasting
Daniel Holmberg, Joel Oskarsson, Erik Wikingsson +2
Ocean dynamics are inherently chaotic, yet existing machine learning ocean models produce only deterministic forecasts. We introduce Njord, a probabilistic data-driven model for oc…
Particle-Guided Diffusion Models for Partial Differential Equations
Andrew Millard, Fredrik Lindsten, Zheng Zhao
We introduce a guided stochastic sampling method that augments sampling from diffusion models with physics-based guidance derived from partial differential equation (PDE) residuals…
DAISI: Data Assimilation with Inverse Sampling using Stochastic Interpolants
Martin Andrae, Erik Wikingsson, So Takao +2
Data assimilation (DA) is a cornerstone of scientific and engineering applications, combining model forecasts with sparse and noisy observations to estimate latent system states. C…
Climate Downscaling with Stochastic Interpolants (CDSI)
Erik Larsson, Ramon Fuentes-Franco, Mikhail Ivanov +1
Global climate projections rely on computationally demanding Earth System Models (ESMs), which are typically limited to coarse spatial resolutions due to their high cost. To obtain…
WyckoffDiff -- A Generative Diffusion Model for Crystal Symmetry
Filip Ekström Kelvinius, Oskar B. Andersson, Abhijith S. Parackal +3
Crystalline materials often exhibit a high level of symmetry. However, most generative models do not account for symmetry, but rather model each atom without any constraints on its…