Paper Citation Record · LEDGER

Generative Lagrangian data assimilation for ocean dynamics under extreme sparsity

As of 14 August 2026, this Paper Citation Record lists 53 of 53 outbound references and 2 inbound Pith citation observations for arXiv:2507.06479.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2507.06479 v1

Coverage vector

measured 53 of 53 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T19:08:49.097598Z

measured 55 of 55 standing notices

One-hop event checks from named stored sources.

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measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T19:08:41.742984Z

measured 1 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

53 of 53 outbound references displayed

verified exact5
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External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation 1a7bc07b-46f9-46cf-953f-fccf03fdd889 · outbound

This paper cites Generative Lagrangian data assimilation for ocean dynamics under extreme sparsity.

Generative Lagrangian data assimilation for ocean dynamics under extreme sparsity Generative Lagrangian data assimilation for ocean dynamics under extreme sparsity

Reference 1

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Generative Lagrangian data assimilation for ocean dynamics under extreme sparsity Unresolved cited work

Reference 2

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Generative Lagrangian data assimilation for ocean dynamics under extreme sparsity Unresolved cited work

Reference 3

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Generative Lagrangian data assimilation for ocean dynamics under extreme sparsity Unresolved cited work

Reference 4

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This paper cites NA and LJ conducted the research and developed the computational models with equal contributions.

Generative Lagrangian data assimilation for ocean dynamics under extreme sparsity NA and LJ conducted the research and developed the computational models with equal contributions

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Reference 6

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This paper cites 2019 Frontiers in Marine Science 6 90.

Generative Lagrangian data assimilation for ocean dynamics under extreme sparsity 2019 Frontiers in Marine Science 6 90

Reference 7

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Reference 8

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Reference 16

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Generative Lagrangian data assimilation for ocean dynamics under extreme sparsity FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators

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Generative Lagrangian data assimilation for ocean dynamics under extreme sparsity GraphCast: Learning skillful medium-range global weather forecasting

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Generative Lagrangian data assimilation for ocean dynamics under extreme sparsity LUCIE: A Lightweight Uncoupled ClImate Emulator with long-term stability and physical consistency for O(1000)-member ensembles

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Generative Lagrangian data assimilation for ocean dynamics under extreme sparsity ACE: A fast, skillful learned global atmospheric model for climate prediction

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This paper cites 2023 arXiv preprint arXiv:2312.12462.

Generative Lagrangian data assimilation for ocean dynamics under extreme sparsity 2023 arXiv preprint arXiv:2312.12462

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Generative Lagrangian data assimilation for ocean dynamics under extreme sparsity Data driven weather forecasts trained and initialised directly from observations

Reference 37

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Generative Lagrangian data assimilation for ocean dynamics under extreme sparsity 2025 Nature 641 1172–1179

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Generative Lagrangian data assimilation for ocean dynamics under extreme sparsity Deep learning-enhanced ensemble-based data assimilation for high-dimensional nonlinear dynamical systems

Reference 39

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Generative Lagrangian data assimilation for ocean dynamics under extreme sparsity Challenges of learning multi-scale dynamics with AI weather models: Implications for stability and one solution

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Generative Lagrangian data assimilation for ocean dynamics under extreme sparsity Advanced Ocean Reanalysis of the Northwestern Atlantic: 1993-2022

Reference 47

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Reference 49

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Generative Lagrangian data assimilation for ocean dynamics under extreme sparsity Integrating Neural Operators with Diffusion Models Improves Spectral Representation in Turbulence Modeling

Reference 50

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Generative Lagrangian data assimilation for ocean dynamics under extreme sparsity Fourier Neural Operator for Parametric Partial Differential Equations

Reference 51

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Generative Lagrangian data assimilation for ocean dynamics under extreme sparsity Broadband Brillouin scattering study of ferroelectric instability of barium sodium niobate

Reference 52

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Generative Lagrangian data assimilation for ocean dynamics under extreme sparsity U-Net in Medical Image Segmentation: A Review of Its Applications Across Modalities

Reference 53

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Generative Lagrangian data assimilation for ocean dynamics under extreme sparsity Simultaneous emulation and downscaling with physically-consistent deep learning-based regional ocean emulators

Reference 54

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Pith citing papers

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