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Paper Citation Record · LEDGER

Scalable Discovery of Fundamental Physical Laws: Learning Magnetohydrodynamics from 3D Turbulence Data

As of 12 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 1 inbound Pith citation observation for arXiv:2501.04094.

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

pith.paper-citation-record.v1
2501.04094 v1

Coverage vector

measured 23 of 23 reference resolution

Typed states for the displayed outbound observations.

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measured 24 of 24 standing notices

One-hop event checks from named stored sources.

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-10T04:55:25.738635Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-10T11:10:09.605267Z

Reference resolution

23 of 23 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 2c698ec4-cfc1-4e4a-bcfb-b61b3445ca96 · outbound

This paper cites Mehta, M.

Scalable Discovery of Fundamental Physical Laws: Learning Magnetohydrodynamics from 3D Turbulence Data Mehta, M

Reference 1

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Observation 72595077-a6fa-4721-808d-838f95650d3e · outbound

This paper cites Astronomia ex machina: a history, primer, and outlook on neural networks in astronomy.

Scalable Discovery of Fundamental Physical Laws: Learning Magnetohydrodynamics from 3D Turbulence Data Astronomia ex machina: a history, primer, and outlook on neural networks in astronomy

Reference 2

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Observation 6c492366-cfe7-4689-9e88-6b5bea52a132 · outbound

This paper cites an unresolved cited work.

Scalable Discovery of Fundamental Physical Laws: Learning Magnetohydrodynamics from 3D Turbulence Data Unresolved cited work

Reference 3

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This paper cites an unresolved cited work.

Scalable Discovery of Fundamental Physical Laws: Learning Magnetohydrodynamics from 3D Turbulence Data Unresolved cited work

Reference 4

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Scalable Discovery of Fundamental Physical Laws: Learning Magnetohydrodynamics from 3D Turbulence Data Unresolved cited work

Reference 5

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Observation 2549abe7-70ec-47b8-ade3-1971ce43a463 · outbound

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Scalable Discovery of Fundamental Physical Laws: Learning Magnetohydrodynamics from 3D Turbulence Data Unresolved cited work

Reference 6

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Observation b86cf4ae-9dc3-4e1a-bec8-017ccfb6237c · outbound

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Scalable Discovery of Fundamental Physical Laws: Learning Magnetohydrodynamics from 3D Turbulence Data Unresolved cited work

Reference 7

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Observation 8565677f-927a-40b0-b8f7-8ad594d5dbeb · outbound

This paper cites Golden, R.

Scalable Discovery of Fundamental Physical Laws: Learning Magnetohydrodynamics from 3D Turbulence Data Golden, R

Reference 8

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Observation 96d8f655-a692-46a7-8772-5094df3c069f · outbound

This paper cites Joshi, S.

Scalable Discovery of Fundamental Physical Laws: Learning Magnetohydrodynamics from 3D Turbulence Data Joshi, S

Reference 9

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Observation 7ddd7ccc-b01f-424f-b26d-de15b1c602a8 · outbound

This paper cites Robertson, Continuum Modeling of Active Nematics via Data-Driven Equation Discovery, Ph.D.

Scalable Discovery of Fundamental Physical Laws: Learning Magnetohydrodynamics from 3D Turbulence Data Robertson, Continuum Modeling of Active Nematics via Data-Driven Equation Discovery, Ph.D

Reference 10

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Scalable Discovery of Fundamental Physical Laws: Learning Magnetohydrodynamics from 3D Turbulence Data Unresolved cited work

Reference 11

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Observation 2a5209d6-315e-4f6e-abdb-15e17970ff0c · outbound

This paper cites Influence of initial conditions on data-driven model identification and information entropy for ideal mhd problems.

Scalable Discovery of Fundamental Physical Laws: Learning Magnetohydrodynamics from 3D Turbulence Data Influence of initial conditions on data-driven model identification and information entropy for ideal mhd problems

Reference 12

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Observation d2e3796a-8f15-4c50-bcf8-f7b50eae119e · outbound

This paper cites The Pencil Code, a modular MPI code for partial differential equations and particles: multipurpose and multiuser-maintained.

Scalable Discovery of Fundamental Physical Laws: Learning Magnetohydrodynamics from 3D Turbulence Data The Pencil Code, a modular MPI code for partial differential equations and particles: multipurpose and multiuser-maintained

Reference 13

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Observation 5a57f6b0-dc8e-4de2-a516-24f421efa256 · outbound

This paper cites Scalable Sparse Regression for Model Discovery: The Fast Lane to Insight.

Scalable Discovery of Fundamental Physical Laws: Learning Magnetohydrodynamics from 3D Turbulence Data Scalable Sparse Regression for Model Discovery: The Fast Lane to Insight

Reference 14

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Observation fbb4b720-c8b2-4b6e-a815-c62247685fb0 · outbound

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Scalable Discovery of Fundamental Physical Laws: Learning Magnetohydrodynamics from 3D Turbulence Data Unresolved cited work

Reference 15

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Observation c336a822-f418-4ba0-bfbd-209a822a0965 · outbound

This paper cites The Weak Form Is Stronger Than You Think.

Scalable Discovery of Fundamental Physical Laws: Learning Magnetohydrodynamics from 3D Turbulence Data The Weak Form Is Stronger Than You Think

Reference 16

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Scalable Discovery of Fundamental Physical Laws: Learning Magnetohydrodynamics from 3D Turbulence Data Unresolved cited work

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Scalable Discovery of Fundamental Physical Laws: Learning Magnetohydrodynamics from 3D Turbulence Data Unresolved cited work

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Observation 5c527a3e-0e53-466d-8695-cd89402ee168 · outbound

This paper cites Santosa and W.

Scalable Discovery of Fundamental Physical Laws: Learning Magnetohydrodynamics from 3D Turbulence Data Santosa and W

Reference 19

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This paper cites Zou and T.

Scalable Discovery of Fundamental Physical Laws: Learning Magnetohydrodynamics from 3D Turbulence Data Zou and T

Reference 20

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This paper cites Bertsimas and W.

Scalable Discovery of Fundamental Physical Laws: Learning Magnetohydrodynamics from 3D Turbulence Data Bertsimas and W

Reference 21

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This paper cites Kaheman, J.

Scalable Discovery of Fundamental Physical Laws: Learning Magnetohydrodynamics from 3D Turbulence Data Kaheman, J

Reference 22

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Scalable Discovery of Fundamental Physical Laws: Learning Magnetohydrodynamics from 3D Turbulence Data word” on σmin by setting each ele- ment of c to zero. We then remove the “word

Reference 23

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Pith citing papers

Observation 69259a95-c049-4c25-9dd4-15ea232c92e2 · inbound

Balance-Guided Sparse Identification of Multiscale Nonlinear PDEs with Small-coefficient Terms cites this paper.

Balance-Guided Sparse Identification of Multiscale Nonlinear PDEs with Small-coefficient Terms Scalable Discovery of Fundamental Physical Laws: Learning Magnetohydrodynamics from 3D Turbulence Data

Reference 36

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