Paper Citation Record · LEDGER

Engineering application of physics-informed neural networks for Saint-Venant torsion

As of 23 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 0 inbound Pith citation observations for arXiv:2505.12389.

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

pith.paper-citation-record.v1
2505.12389 v1

Coverage vector

measured 23 of 23 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:43:28.998497Z

measured 23 of 23 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+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

23 of 23 outbound references displayed

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verified fuzzy14
unresolved8
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d49a77d7-10f9-465d-b69f-f5ccabf0f820 · outbound

This paper cites Pearlmutter, Alexey Andreyevich Radul, and Jeffrey Mark Siskind.

Engineering application of physics-informed neural networks for Saint-Venant torsion Pearlmutter, Alexey Andreyevich Radul, and Jeffrey Mark Siskind

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:43:30.198981Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 7e9ec804-7053-4d90-bfd4-2b98367f11b9 · outbound

This paper cites Saint venant’s torsion by the finite-volume method.

Engineering application of physics-informed neural networks for Saint-Venant torsion Saint venant’s torsion by the finite-volume method

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:43:30.183070Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 441cefe3-6b3e-4778-aa68-930df983e6e1 · outbound

This paper cites Parameterized Physics-informed Neural Networks for Parameterized PDEs.

Engineering application of physics-informed neural networks for Saint-Venant torsion Parameterized Physics-informed Neural Networks for Parameterized PDEs

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-15T20:43:28.588332Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3c64f949-0c0a-4fcc-8c47-a536ca8af582 · outbound

This paper cites Prandtl’s formulation for the saint--venant’s torsion of homogeneous piezoelectric beams.

Engineering application of physics-informed neural networks for Saint-Venant torsion Prandtl’s formulation for the saint--venant’s torsion of homogeneous piezoelectric beams

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:43:30.090841Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation d912fbbd-48ca-4635-8f67-6a3fee542abe · outbound

This paper cites Some analytical solutions for saint-venant torsion of non-homogeneous cylindrical bars.

Engineering application of physics-informed neural networks for Saint-Venant torsion Some analytical solutions for saint-venant torsion of non-homogeneous cylindrical bars

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:43:29.979077Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 8f2efbaf-567c-4de7-ae82-b133ef5def09 · outbound

This paper cites Torsion of a non-circular bar.

Engineering application of physics-informed neural networks for Saint-Venant torsion Torsion of a non-circular bar

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:43:29.961991Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 9888c3b1-394a-45bf-9cad-51c0fa9ae2c5 · outbound

This paper cites Cross-sectional analysis of beams subjected to saint-venant torsion using the green’s theorem and the finite difference method, 2022.

Engineering application of physics-informed neural networks for Saint-Venant torsion Cross-sectional analysis of beams subjected to saint-venant torsion using the green’s theorem and the finite difference method, 2022

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:43:29.872826Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 75454929-8b6a-4f9f-adac-5d9fe7452a35 · outbound

This paper cites Nvidia simnet : An ai-accelerated multi-physics simulation framework.

Engineering application of physics-informed neural networks for Saint-Venant torsion Nvidia simnet : An ai-accelerated multi-physics simulation framework

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:43:29.798552Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 1d69a8fc-f737-4beb-8cbf-3dbfee7eadae · outbound

This paper cites Galerkin solutions for the saint-venant torsion of prismatic bars with rectangular crosssections.

Engineering application of physics-informed neural networks for Saint-Venant torsion Galerkin solutions for the saint-venant torsion of prismatic bars with rectangular crosssections

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:43:29.781813Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 96540063-a7ef-4ecb-96c0-a941a3a7388e · outbound

This paper cites Solving saint venant torsion problems for rectangular beams using single finite fourier sine transform method.

Engineering application of physics-informed neural networks for Saint-Venant torsion Solving saint venant torsion problems for rectangular beams using single finite fourier sine transform method

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:43:29.734972Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation e650f869-b849-47ff-a91a-c163262c8547 · outbound

This paper cites Double finite sine transform method for saint-venant torsional analysis of beams with rectangular cross-section.

Engineering application of physics-informed neural networks for Saint-Venant torsion Double finite sine transform method for saint-venant torsional analysis of beams with rectangular cross-section

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:43:29.622232Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 5a2b909b-1c11-4d4b-8c10-727d14794645 · outbound

This paper cites Extended physics-informed neural networks (xpinns): A generalized space-time domain decomposition based deep learning framework for nonlinear partial differential equations.

Engineering application of physics-informed neural networks for Saint-Venant torsion Extended physics-informed neural networks (xpinns): A generalized space-time domain decomposition based deep learning framework for nonlinear partial differential equations

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-15T20:43:28.708072Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 44eea4b6-291c-4bad-9f16-737cd3616185 · outbound

This paper cites A finite element method for the saint-venant torsion and bending problems for prismatic beams.

Engineering application of physics-informed neural networks for Saint-Venant torsion A finite element method for the saint-venant torsion and bending problems for prismatic beams

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:43:29.592215Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 0fc7ffc9-10b0-4661-b882-ceeeb9db7528 · outbound

This paper cites Physics-informed machine learning.

Engineering application of physics-informed neural networks for Saint-Venant torsion Physics-informed machine learning

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-15T20:43:28.793763Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a5889091-b28b-4563-af23-9bf8926f5065 · outbound

This paper cites Vs-pinn: A fast and efficient training of physics-informed neural networks using variable-scaling methods for solving pdes with stiff behavior.

Engineering application of physics-informed neural networks for Saint-Venant torsion Vs-pinn: A fast and efficient training of physics-informed neural networks using variable-scaling methods for solving pdes with stiff behavior

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:43:29.444336Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation d5313fee-0820-4c71-8705-73313a2601bd · outbound

This paper cites The Deep Ritz Method for Parametric $p$-Dirichlet Problems.

Engineering application of physics-informed neural networks for Saint-Venant torsion The Deep Ritz Method for Parametric $p$-Dirichlet Problems

Reference 16

Resolution: verified exact; local_arxiv, observed 2026-08-15T20:43:29.134830Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation a638cc45-24d7-468b-a8b8-5c12d711864e · outbound

This paper cites hp-vpinns: Variational physics-informed neural networks with domain decomposition.

Engineering application of physics-informed neural networks for Saint-Venant torsion hp-vpinns: Variational physics-informed neural networks with domain decomposition

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-15T20:43:28.810723Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2dee9e01-42cb-4555-920e-9dbf5c6e37c2 · outbound

This paper cites Learning nonlinear operators via deeponet based on the universal approximation theorem of operators.

Engineering application of physics-informed neural networks for Saint-Venant torsion Learning nonlinear operators via deeponet based on the universal approximation theorem of operators

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-15T20:43:28.815094Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5cb82ec3-9c61-4e03-b409-03b683faac6d · outbound

This paper cites Fourier neural operator for parametric partial differential equations.

Engineering application of physics-informed neural networks for Saint-Venant torsion Fourier neural operator for parametric partial differential equations

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:43:29.408117Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 797a5010-9673-4807-b7ea-1ce014001407 · outbound

This paper cites Reduced-PINN: An Integration-Based Physics-Informed Neural Networks for Stiff ODEs.

Engineering application of physics-informed neural networks for Saint-Venant torsion Reduced-PINN: An Integration-Based Physics-Informed Neural Networks for Stiff ODEs

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-15T20:43:28.903921Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1b56a1b0-8c32-4a44-a5c0-bccef284aa6f · outbound

This paper cites Automatic differentiation in pytorch.

Engineering application of physics-informed neural networks for Saint-Venant torsion Automatic differentiation in pytorch

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-15T20:43:28.988312Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e8d588da-4a83-4e7e-9cb1-ce9ea531ae4d · outbound

This paper cites Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations.

Engineering application of physics-informed neural networks for Saint-Venant torsion Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-15T20:43:28.992985Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 92c477aa-a4d1-410b-a5f2-47e5ffc32956 · outbound

This paper cites A-pinn: Auxiliary physics informed neural networks for forward and inverse problems of nonlinear integro-differential equations.

Engineering application of physics-informed neural networks for Saint-Venant torsion A-pinn: Auxiliary physics informed neural networks for forward and inverse problems of nonlinear integro-differential equations

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:43:29.242993Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Pith citing papers

No inbound Pith citation observations are available.