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Paper Citation Record · LEDGER

Learned Coarse Models for Efficient Turbulence Simulation

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2112.15275.

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

pith.paper-citation-record.v1
2112.15275 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T00:16:45.723802Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-13T06:07:22.196022Z

Reference resolution

0 of 0 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation e49a1064-0fec-47a4-8623-9305aaf36eca · inbound

Geometric and Physical Constraints Synergistically Enhance Neural PDE Surrogates cites this paper.

Geometric and Physical Constraints Synergistically Enhance Neural PDE Surrogates Learned Coarse Models for Efficient Turbulence Simulation

Reference 66

Resolution
unresolved
no resolver link, observed 2026-08-07T10:30:12.998844Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T10:30:12.998844Z digest=sha256:fac6240892f3f35212c32984447f6e245f28b08269229cdf7f7d185a777f0fe1

Observation faf03485-c24a-40a5-8ff6-820d6e165928 · inbound

SlotPi: Physics-informed Object-centric Reasoning Models cites this paper.

SlotPi: Physics-informed Object-centric Reasoning Models Learned Coarse Models for Efficient Turbulence Simulation

Reference 67

Resolution
unresolved
no resolver link, observed 2026-08-07T04:25:21.184321Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 4e8aabff-8c94-4437-8dcb-72fe36e9b132 · inbound

What drives the growth of black holes: a decade of progress cites this paper.

What drives the growth of black holes: a decade of progress Learned Coarse Models for Efficient Turbulence Simulation

Reference 97

Resolution
unresolved
no resolver link, observed 2026-08-06T23:10:50.641596Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:10:50.641596Z digest=sha256:99a3f9b07f9a884fcd980a9091ff384ff6e79b9c4e2c89d7b3f6fd1fe136dca5

Observation 5c2d0861-0a78-4efb-8117-24e5508b0787 · inbound

One Scale at a Time: Scale-Autoregressive Modeling for Fluid Flow Distributions cites this paper.

One Scale at a Time: Scale-Autoregressive Modeling for Fluid Flow Distributions Learned Coarse Models for Efficient Turbulence Simulation

Reference 16

Resolution
verified exact
arxiv_id, observed 2026-05-11T09:16:03.673652Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-10T16:07:37.663509Z digest=sha256:fea5bac490345954120227d3575dde01cb865f4b42ce9b53e8019da9c89c43ef

Observation 5fb5319d-f4e7-4853-b719-8fe56d022228 · inbound

Acceleration of horizontal numerical advection for atmospheric modeling through surrogate modeling with temporal coarse-graining cites this paper.

Acceleration of horizontal numerical advection for atmospheric modeling through surrogate modeling with temporal coarse-graining Learned Coarse Models for Efficient Turbulence Simulation

Reference 6

Resolution
verified exact
arxiv_id, observed 2026-05-13T06:07:22.198501Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-05-13T06:06:53.218538Z digest=sha256:7dd5d949a4aa48d43a1faf84608aa9cceee851d4b31dbfa9891dc2b4a68092dd

Observation 1550945c-7167-463b-b0e8-ea94b807c42e · inbound

TIDE: A Physically Diverse 3D Turbulence Benchmark Dataset for Advancing Scientific Machine Learning cites this paper.

TIDE: A Physically Diverse 3D Turbulence Benchmark Dataset for Advancing Scientific Machine Learning Learned Coarse Models for Efficient Turbulence Simulation

Reference 45

Resolution
unresolved
no resolver link, observed 2026-08-08T00:16:45.723802Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T00:16:45.723802Z digest=sha256:4e87bf2029ea0f062a574569a868c8dca5cd510c42061a1637d2d17ccfd92e61