New method uses learned surrogates for data assimilation

By PulseAugur Editorial · [2 sources] · 2026-05-30 02:15

Researchers have developed a new method for continuous data assimilation using learned surrogate models of dynamical systems. This approach addresses challenges where system dynamics are unknown or computationally expensive to simulate. The analysis shows that using surrogate models maintains exponential convergence, with an error floor dependent on approximation and noise levels. The study also quantifies the training data required for accurate assimilation. AI

RANK_REASON The cluster contains a research paper published on arXiv detailing a new methodology.

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arXiv stat.ML TIER_1 English(EN) · Wenwen Li, Daniel Sanz-Alonso · 2026-06-02 04:00

Continuous Data Assimilation with Learned Surrogate Dynamics

arXiv:2606.00480v1 Announce Type: cross Abstract: Continuous data assimilation seeks to estimate the state of a dynamical system from partial observations. In many applications, however, the state dynamics are unknown or prohibitively expensive to simulate at the required resolut…
arXiv stat.ML TIER_1 English(EN) · Daniel Sanz-Alonso · 2026-05-30 02:15

Continuous Data Assimilation with Learned Surrogate Dynamics

Continuous data assimilation seeks to estimate the state of a dynamical system from partial observations. In many applications, however, the state dynamics are unknown or prohibitively expensive to simulate at the required resolution, leading to model error. Motivated by this cha…

COVERAGE [2]

Continuous Data Assimilation with Learned Surrogate Dynamics

Continuous Data Assimilation with Learned Surrogate Dynamics

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