mlsanityData to 14 Oct 2026

Probabilistic Forecasting for Dynamical Systems with Missing or Imperfect Data

arXiv 2503.12273 ↗First posted 15 Mar 2025physics.comp-ph cs.LG math.DScited by 1

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Abstract (arXiv)

The modeling of dynamical systems is essential in many fields, but applying machine learning techniques is often challenging due to incomplete or noisy data. This study introduces a variant of stochastic interpolation (SI) for probabilistic forecasting, estimating future states as distributions rather than single-point predictions. We explore its mathematical foundations and demonstrate its effectiveness on various dynamical systems, including the challenging WeatherBench dataset.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 28 of its 52 references that are in the map. First posted on arXiv 15 Mar 2025.

Builds on

28 of 52 references.

References · 28

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16 more
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Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025Reframing Generative Models for Physical Systems using Stochastic Interpolantscited by 2