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Paper Citation Record · LEDGER

Physics-Informed Neural Networks for High-Precision Grad-Shafranov Equilibrium Reconstruction

As of 9 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 0 inbound Pith citation observations for arXiv:2507.16636.

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

pith.paper-citation-record.v1
2507.16636 v1

Coverage vector

measured 32 of 32 reference resolution

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Reference resolution

32 of 32 outbound references displayed

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External citation measurements

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Outbound references

Observation 72d871ce-3f23-4621-9918-1cd6e2efefbc · outbound

This paper cites an unresolved cited work.

Physics-Informed Neural Networks for High-Precision Grad-Shafranov Equilibrium Reconstruction Unresolved cited work

Reference 1

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This paper cites an unresolved cited work.

Physics-Informed Neural Networks for High-Precision Grad-Shafranov Equilibrium Reconstruction Unresolved cited work

Reference 2

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Observation 5d97c8e2-f02c-4e97-a082-6c16b566b272 · outbound

This paper cites Shafranov, On magnetohydrodynamical equilibrium configurations, Soviet Physics JETP 6 (1958) 1013.

Physics-Informed Neural Networks for High-Precision Grad-Shafranov Equilibrium Reconstruction Shafranov, On magnetohydrodynamical equilibrium configurations, Soviet Physics JETP 6 (1958) 1013

Reference 3

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Physics-Informed Neural Networks for High-Precision Grad-Shafranov Equilibrium Reconstruction Unresolved cited work

Reference 4

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Observation f5c878c3-1076-4f17-b55f-56e987da630d · outbound

This paper cites Albanese, A.

Physics-Informed Neural Networks for High-Precision Grad-Shafranov Equilibrium Reconstruction Albanese, A

Reference 5

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Observation b40bac64-a3ae-44d1-bbe4-c91fb344f6c8 · outbound

This paper cites Ricketson, A.

Physics-Informed Neural Networks for High-Precision Grad-Shafranov Equilibrium Reconstruction Ricketson, A

Reference 6

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Observation 3839c344-9bf7-4efe-9adc-6b01df3ec672 · outbound

This paper cites Heumann, F.

Physics-Informed Neural Networks for High-Precision Grad-Shafranov Equilibrium Reconstruction Heumann, F

Reference 7

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Physics-Informed Neural Networks for High-Precision Grad-Shafranov Equilibrium Reconstruction Unresolved cited work

Reference 8

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Observation bbfbf83a-6010-4c49-b179-4ec780a7c68c · outbound

This paper cites Palha, B.

Physics-Informed Neural Networks for High-Precision Grad-Shafranov Equilibrium Reconstruction Palha, B

Reference 9

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This paper cites doi:https://doi.org/10.1016/j.cpc.2014.02.008.

Physics-Informed Neural Networks for High-Precision Grad-Shafranov Equilibrium Reconstruction doi:https://doi.org/10.1016/j.cpc.2014.02.008

Reference 10

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Observation 5bc93190-3efc-41c9-94dc-01bcc98acefd · outbound

This paper cites Fambri, E.

Physics-Informed Neural Networks for High-Precision Grad-Shafranov Equilibrium Reconstruction Fambri, E

Reference 11

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Observation f0cc0321-3111-45e7-a0b6-45732009ff38 · outbound

This paper cites Zampa, S.

Physics-Informed Neural Networks for High-Precision Grad-Shafranov Equilibrium Reconstruction Zampa, S

Reference 12

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Physics-Informed Neural Networks for High-Precision Grad-Shafranov Equilibrium Reconstruction Unresolved cited work

Reference 13

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This paper cites Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations.

Physics-Informed Neural Networks for High-Precision Grad-Shafranov Equilibrium Reconstruction Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations

Reference 14

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Observation 659c8998-6279-4f88-94b6-090ec2ead271 · outbound

This paper cites Raissi, P.

Physics-Informed Neural Networks for High-Precision Grad-Shafranov Equilibrium Reconstruction Raissi, P

Reference 15

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Physics-Informed Neural Networks for High-Precision Grad-Shafranov Equilibrium Reconstruction Unresolved cited work

Reference 16

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Observation 670b6d02-a12a-4932-8325-c45f180313c1 · outbound

This paper cites Joung, J.

Physics-Informed Neural Networks for High-Precision Grad-Shafranov Equilibrium Reconstruction Joung, J

Reference 17

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Physics-Informed Neural Networks for High-Precision Grad-Shafranov Equilibrium Reconstruction Merlo, D

Reference 18

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Reference 21

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Reference 22

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Observation cebe93b7-5d58-40f0-927b-2b3f8d69260f · outbound

This paper cites Evaluation and Verification of Physics-Informed Neural Models of the Grad-Shafranov Equation.

Physics-Informed Neural Networks for High-Precision Grad-Shafranov Equilibrium Reconstruction Evaluation and Verification of Physics-Informed Neural Models of the Grad-Shafranov Equation

Reference 23

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This paper cites Wang, C.-Y.

Physics-Informed Neural Networks for High-Precision Grad-Shafranov Equilibrium Reconstruction Wang, C.-Y

Reference 24

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This paper cites Krishnapriyan, A.

Physics-Informed Neural Networks for High-Precision Grad-Shafranov Equilibrium Reconstruction Krishnapriyan, A

Reference 26

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Observation ce82f4b7-9b71-4989-b91f-2efcdf38bd10 · outbound

This paper cites Hu, Notes on tokamak equilibrium*.

Physics-Informed Neural Networks for High-Precision Grad-Shafranov Equilibrium Reconstruction Hu, Notes on tokamak equilibrium*

Reference 27

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This paper cites Loh, On latin hypercube sampling, The Annals of Statistics 24 (5) (1996) 2058–2080.doi:10.1214/aos/1069362310.

Physics-Informed Neural Networks for High-Precision Grad-Shafranov Equilibrium Reconstruction Loh, On latin hypercube sampling, The Annals of Statistics 24 (5) (1996) 2058–2080.doi:10.1214/aos/1069362310

Reference 28

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Physics-Informed Neural Networks for High-Precision Grad-Shafranov Equilibrium Reconstruction Adam: A Method for Stochastic Optimization

Reference 29

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Reference 30

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Physics-Informed Neural Networks for High-Precision Grad-Shafranov Equilibrium Reconstruction Deriaz, B

Reference 31

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This paper cites A hands-on introduction to Physics-Informed Neural Networks for solving partial differential equations with benchmark tests taken from astrophysics and plasma physics.

Physics-Informed Neural Networks for High-Precision Grad-Shafranov Equilibrium Reconstruction A hands-on introduction to Physics-Informed Neural Networks for solving partial differential equations with benchmark tests taken from astrophysics and plasma physics

Reference 32

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Pith citing papers

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