Publications

2026

1. 

   DAISI: Data Assimilation with Inverse Sampling using Stochastic Interpolants

   Martin Andrae, Erik Larsson, So Takao, Tomas Landelius, and Fredrik Lindsten

   In Proceedings of the 43rd International Conference on Machine Learning, 2026

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   Data assimilation (DA) is a cornerstone of scientific and engineering applications, combining model forecasts with sparse and noisy observations to estimate latent system states. Classical high-dimensional DA methods, such as the ensemble Kalman filter, rely on Gaussian approximations that are violated for complex dynamics or observation operators. To address this limitation, we introduce DAISI, a scalable filtering algorithm built on flow-based generative models that enables flexible probabilistic inference using data-driven priors. The core idea is to use a stationary, pre-trained generative prior that first incorporates forecast information through a novel inverse-sampling step, before assimilating observations via guidance-based conditional sampling. This allows us to leverage any forecasting model as part of the DA pipeline without having to retrain or fine-tune the generative prior at each assimilation step. Experiments on challenging nonlinear systems show that DAISI achieves accurate filtering results in regimes with sparse, noisy, and nonlinear observations where traditional methods struggle.

   @inproceedings{andrae2026daisi,
     title = {DAISI: Data Assimilation with Inverse Sampling using Stochastic Interpolants},
     author = {Andrae, Martin and Larsson, Erik and Takao, So and Landelius, Tomas and Lindsten, Fredrik},
     booktitle = {Proceedings of the 43rd International Conference on Machine Learning},
     year = {2026},
   }

2025

1. 

   Continuous Ensemble Weather Forecasting with Diffusion models

   Martin Andrae, Tomas Landelius, Joel Oskarsson, and Fredrik Lindsten

   In The Thirteenth International Conference on Learning Representations, 2025

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   Weather forecasting has seen a shift in methods from numerical simulations to data-driven systems. While initial research in the area focused on deterministic forecasting, recent works have used diffusion models to produce skillful ensemble forecasts. These models are trained on a single forecasting step and rolled out autoregressively. However, they are computationally expensive and accumulate errors for high temporal resolution due to the many rollout steps. We address these limitations with Continuous Ensemble Forecasting, a novel and flexible method for sampling ensemble forecasts in diffusion models. The method can generate temporally consistent ensemble trajectories completely in parallel, with no autoregressive steps. Continuous Ensemble Forecasting can also be combined with autoregressive rollouts to yield forecasts at an arbitrary fine temporal resolution without sacrificing accuracy. We demonstrate that the method achieves competitive results for global weather forecasting with good probabilistic properties.

   @inproceedings{andrae2025continuous,
     title = {Continuous Ensemble Weather Forecasting with Diffusion models},
     author = {Andrae, Martin and Landelius, Tomas and Oskarsson, Joel and Lindsten, Fredrik},
     booktitle = {The Thirteenth International Conference on Learning Representations},
     year = {2025},
   }