Paper Citation Record · LEDGER

Learned Coarse Models for Efficient Turbulence Simulation

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 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 11 of 11 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 11 of 11 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T04:15:02.943136Z

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

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

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 509ec916-f661-4a20-98fc-1034e9805123 · inbound

A data-driven learned discretization approach in finite volume schemes for hyperbolic conservation laws and varying boundary conditions cites this paper.

A data-driven learned discretization approach in finite volume schemes for hyperbolic conservation laws and varying boundary conditions Learned Coarse Models for Efficient Turbulence Simulation

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-11T18:49:14.117876Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 80894dd3-56a6-4844-ace8-b7192db5f7b1 · inbound

Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates cites this paper.

Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates Learned Coarse Models for Efficient Turbulence Simulation

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-11T13:31:12.097136Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0e2d1ecc-7f25-4171-8d4a-dd421f590cd8 · inbound

Learning Effective Dynamics across Spatio-Temporal Scales of Complex Flows cites this paper.

Learning Effective Dynamics across Spatio-Temporal Scales of Complex Flows Learned Coarse Models for Efficient Turbulence Simulation

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-08T11:17:12.868369Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 856dc51c-31dd-4f1c-ad51-d80b33a44c82 · inbound

PeSANet: Physics-encoded Spectral Attention Network for Simulating PDE-Governed Complex Systems cites this paper.

PeSANet: Physics-encoded Spectral Attention Network for Simulating PDE-Governed Complex Systems Learned Coarse Models for Efficient Turbulence Simulation

Reference 2015

Resolution: unresolved; no resolver link, observed 2026-08-16T04:15:02.943136Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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.

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.

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.

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 2026-08-21T06:32:19.484+00:00.

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 2026-08-21T06:32:19.484+00:00.

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.

Observation 2367ac2d-485b-4646-8bf6-5d72db959306 · inbound

Fluid-DiT: Graph-Free Diffusion Transformers for Fluid Flow Simulations Learning cites this paper.

Fluid-DiT: Graph-Free Diffusion Transformers for Fluid Flow Simulations Learning Learned Coarse Models for Efficient Turbulence Simulation

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-15T14:31:29.770969Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.