Njord, a probabilistic data-driven model for ocean forecasting, applicable to both global and regional domains, is introduced, combining a deep latent variable framework with a graph neural network architecture, enabling sampling each forecast step in a single forward pass.
Daniel Holmberg, Joel Oskarsson, Erik Wikingsson et al.· arXiv.org· 2 citations
Flow- and diffusion-based generative models have recently emerged as flexible and highly efficient forecasting models for dynamical systems. When combined with inference-time guidance, they offer a promising route to high-dimensional non-Gaussian data assimilation (DA), the problem of combining forecasts with observati...
Erik Wikingsson, Martin Andrae, Tomas Landelius et al.· 0 citations
Existing machine learning weather forecasting models typically generate forecasts through autoregressive rollouts at a fixed temporal resolution. While highly efficient for long-range prediction, this formulation can suffer from severe error accumulation when used with shorter time steps and does not explicitly encode...
Maria Marchenko, Martin Andrae, F. Lindsten et al.· 0 citations
Inference-time steering adapts pretrained diffusion and flow-based models to new tasks, e.g., to generate samples from a conditional distribution or samples with desired properties, without retraining. This can be formalized as sampling from a reward-tilted generative prior. As exact sampling from this distribution is...
Adhithyan Kalaivanan, Zheng Zhao, Jens Sjölund et al.· 0 citations
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