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Paper Citation Record · LEDGER

Non-Linear Super-Stencils for Turbulence Model Corrections

As of 13 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 0 inbound Pith citation observations for arXiv:2411.16493.

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

pith.paper-citation-record.v1
2411.16493 v3

Coverage vector

measured 27 of 27 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T13:07:49.349585Z

measured 27 of 27 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

27 of 27 outbound references displayed

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External citation measurements

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Outbound references

Observation 42e2e6f0-c0c6-4e82-a0a0-4e7470570ca1 · outbound

This paper cites an unresolved cited work.

Non-Linear Super-Stencils for Turbulence Model Corrections Unresolved cited work

Reference 1

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Observation 08b406dc-bace-4ae7-bc62-d88467fb4e5f · outbound

This paper cites an unresolved cited work.

Non-Linear Super-Stencils for Turbulence Model Corrections Unresolved cited work

Reference 2

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Observation 1389607a-ee06-4bbc-b577-d4cd57189280 · outbound

This paper cites Turbulence Modeling in the Age of Data.Annual Review of Fluid Mechanics , 51(1):357–377, 2019.

Non-Linear Super-Stencils for Turbulence Model Corrections Turbulence Modeling in the Age of Data.Annual Review of Fluid Mechanics , 51(1):357–377, 2019

Reference 3

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Observation 3c1ce439-4fd7-4e73-99c2-d560eaedadd2 · outbound

This paper cites DNS-Based Turbulent Closures for Sediment Transport Using Symbolic Regression.Flow, Turbulence and Combustion, 112(1):217–241, January 2024.

Non-Linear Super-Stencils for Turbulence Model Corrections DNS-Based Turbulent Closures for Sediment Transport Using Symbolic Regression.Flow, Turbulence and Combustion, 112(1):217–241, January 2024

Reference 4

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Observation 8b586c9f-ca91-4684-9274-cb3850ca986f · outbound

This paper cites A wall model for separated flows: embedded learning to improve a posteriori performance.

Non-Linear Super-Stencils for Turbulence Model Corrections A wall model for separated flows: embedded learning to improve a posteriori performance

Reference 5

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Observation 3f66fb5d-8130-4b4b-abc1-c744ef33859d · outbound

This paper cites Active learning of data-assimilation closures using Graph Neural Networks.

Non-Linear Super-Stencils for Turbulence Model Corrections Active learning of data-assimilation closures using Graph Neural Networks

Reference 6

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Observation 9758972e-886f-4e67-89c1-334b5bc5ff80 · outbound

This paper cites Frame-independent vector-cloud neural network for nonlocal con- stitutive modeling on arbitrary grids.

Non-Linear Super-Stencils for Turbulence Model Corrections Frame-independent vector-cloud neural network for nonlocal con- stitutive modeling on arbitrary grids

Reference 7

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Observation d52759fd-ad30-48d0-bbd9-c9fb868c2ae3 · outbound

This paper cites The Graph Neural Network Model.

Non-Linear Super-Stencils for Turbulence Model Corrections The Graph Neural Network Model

Reference 8

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Observation d1f6d00c-5350-4964-b61f-aaa54152f185 · outbound

This paper cites Reynolds averaged turbulence modelling using deep neural networks with embedded invariance.

Non-Linear Super-Stencils for Turbulence Model Corrections Reynolds averaged turbulence modelling using deep neural networks with embedded invariance

Reference 9

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Observation d9d60dc3-6029-4604-a906-cf99f2395ee7 · outbound

This paper cites Boureima, V.

Non-Linear Super-Stencils for Turbulence Model Corrections Boureima, V

Reference 10

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Observation 9fce74be-01db-4120-9b62-f8ed2ce53b85 · outbound

This paper cites Boussinesq.

Non-Linear Super-Stencils for Turbulence Model Corrections Boussinesq

Reference 11

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Observation f5326fcd-e9b8-4742-88fe-f33d2660b8ca · outbound

This paper cites an unresolved cited work.

Non-Linear Super-Stencils for Turbulence Model Corrections Unresolved cited work

Reference 12

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Observation 6538c76f-96b3-4529-ba40-3dbffc8b3413 · outbound

This paper cites an unresolved cited work.

Non-Linear Super-Stencils for Turbulence Model Corrections Unresolved cited work

Reference 13

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Observation 68d16a5a-baa3-4acc-be7d-5b8ad5623d47 · outbound

This paper cites Variational assimilation of sparse time-averaged data for efficient adjoint-based optimization of unsteady rans simulations.

Non-Linear Super-Stencils for Turbulence Model Corrections Variational assimilation of sparse time-averaged data for efficient adjoint-based optimization of unsteady rans simulations

Reference 14

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Observation d9236ae5-6e14-454a-9083-f982dfd57843 · outbound

This paper cites Flows over periodic hills of parameterized geometries: A dataset for data-driven turbulence modeling from direct simulations.

Non-Linear Super-Stencils for Turbulence Model Corrections Flows over periodic hills of parameterized geometries: A dataset for data-driven turbulence modeling from direct simulations

Reference 15

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Observation e6c8368d-021a-4b4e-9aaf-9eecc6d14dfc · outbound

This paper cites Large Eddy Simulation Requirements for the Flow over Periodic Hills.

Non-Linear Super-Stencils for Turbulence Model Corrections Large Eddy Simulation Requirements for the Flow over Periodic Hills

Reference 16

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Observation 2f31e2ba-5aec-4578-8ea4-63439e6c01f4 · outbound

This paper cites A thorough description of how wall functions are implemented in openfoam.

Non-Linear Super-Stencils for Turbulence Model Corrections A thorough description of how wall functions are implemented in openfoam

Reference 17

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Observation e8a8acec-25a6-4d99-b956-b6bac8e35258 · outbound

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Non-Linear Super-Stencils for Turbulence Model Corrections Unresolved cited work

Reference 18

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Observation 68c20830-1b4b-4cdd-b1e4-e637ce33b562 · outbound

This paper cites In Nu- merical prediction of flow, heat transfer, turbulence and combustion , pages 54–73.

Non-Linear Super-Stencils for Turbulence Model Corrections In Nu- merical prediction of flow, heat transfer, turbulence and combustion , pages 54–73

Reference 19

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Observation 28fa73c0-1ddb-410c-b3a0-84fb2ccafe4c · outbound

This paper cites Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, St´ efan J.

Non-Linear Super-Stencils for Turbulence Model Corrections Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, St´ efan J

Reference 20

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Observation dd9c15bd-8b68-4524-a282-d34a551b5e3f · outbound

This paper cites Torchscript: Optimized execution of pytorch programs.

Non-Linear Super-Stencils for Turbulence Model Corrections Torchscript: Optimized execution of pytorch programs

Reference 21

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Observation ec7d2d3f-b134-4373-a9e1-d07c0941f74a · outbound

This paper cites Multilayer feedforward networks are universal approx- imators.

Non-Linear Super-Stencils for Turbulence Model Corrections Multilayer feedforward networks are universal approx- imators

Reference 22

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Observation d5855ee4-ca05-4e65-aa53-8a24575f0af1 · outbound

This paper cites Highway networks, 2015.

Non-Linear Super-Stencils for Turbulence Model Corrections Highway networks, 2015

Reference 23

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Observation 946663d5-9163-4d63-a755-391f74e07c8f · outbound

This paper cites Batch normalization: Accelerating deep network training by reducing internal covariate shift, 2015.

Non-Linear Super-Stencils for Turbulence Model Corrections Batch normalization: Accelerating deep network training by reducing internal covariate shift, 2015

Reference 24

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Observation e6e21ca5-cc98-40ef-8ffd-656c8abed72e · outbound

This paper cites Pytorch: An imperative style, high-performance deep learning library.

Non-Linear Super-Stencils for Turbulence Model Corrections Pytorch: An imperative style, high-performance deep learning library

Reference 25

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Observation 44a3c15a-b10a-42f5-874f-6d5988d78058 · outbound

This paper cites Pytorch 2: Faster machine learning through dynamic python bytecode transformation and graph compilation.

Non-Linear Super-Stencils for Turbulence Model Corrections Pytorch 2: Faster machine learning through dynamic python bytecode transformation and graph compilation

Reference 26

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Source-reported events for the cited work

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Observation 3339768e-f350-462e-b983-bd3e9036fc73 · outbound

This paper cites On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima.

Non-Linear Super-Stencils for Turbulence Model Corrections On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima

Reference 27

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Pith citing papers

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