Author

F. Lindsten

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#machine learning Preprint Oct 2026

DAWIS: Data Assimilation with Windowed Inverse Sampling via Multitask Interpolants

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

#machine learning Preprint Sep 2026

Interacting particle guidance for sampling reward-tilted generative priors

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