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Prépublication · 2026 arXiv EN

ENFR: Evidence Networks with Flow-Matched References for Amortized Simulation-Based Model Inference

The evidence, $\mathcal{Z} = p(x | \mathcal{M})$, of a data-generating model $\mathcal{M}$ is a powerful tool in Bayesian frameworks: it enables model inference, model misspecification tests, and goodness-of-fit assessment as an alternative to standard discrep...

Kai Lehman, Jameson Dong et al. #stat.ML

Prépublication · 2026 arXiv EN

Learning without relearning physical symmetry: an SO(3)-equivariant convolutional network (CNN) on the sphere

Many of the most important datasets in the physical sciences live natively on the sphere, and these data often carry no preferred position or orientation. Using methods that do not reflect this symmetry leads to far higher simulation requirements, which can be...

Joshua Williamson, Niall Jeffrey et al. #stat.ML

Prépublication · 2026 arXiv EN

Accounting for model error in ensemble variational data assimilation through learned generative dynamics

Many approaches have recently been proposed to combine data assimilation with machine learning for improved forecasts with geophysical models. Here, we focus on combining advanced sequential data assimilation schemes with a generative forecasting model which a...

Marc Bocquet, Tobias S. Finn et al. #stat.ML