Paper Citation Record · LEDGER

Finite Volume Physical Informed Neural Network (FV-PINN) with Reduced Derivative Order for Incompressible Flows

As of 12 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 0 inbound Pith citation observations for arXiv:2411.17095.

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

pith.paper-citation-record.v1
2411.17095 v1

Coverage vector

measured 30 of 30 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T12:35:11.818566Z

measured 30 of 30 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.

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measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

30 of 30 outbound references displayed

verified exact0
verified fuzzy26
unresolved4
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External citation measurements

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Outbound references

Observation fa978d54-ed4a-408c-a539-b4d45e9d9936 · outbound

This paper cites This integration enhances model interpretability and ensure s that predictions are more consistent with physical laws.

Finite Volume Physical Informed Neural Network (FV-PINN) with Reduced Derivative Order for Incompressible Flows This integration enhances model interpretability and ensure s that predictions are more consistent with physical laws

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:35:12.298424Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation d93e123f-8b3f-4932-ad5f-5b43deee2300 · outbound

This paper cites an unresolved cited work.

Finite Volume Physical Informed Neural Network (FV-PINN) with Reduced Derivative Order for Incompressible Flows Unresolved cited work

Reference 2

Resolution: unresolved; raw_fallback, observed 2026-08-12T12:35:12.283516Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 577f5365-a86d-434f-b59e-951bc231380d · outbound

This paper cites This transformation facilitates the handling of complex bound- aries and discontinuities.

Finite Volume Physical Informed Neural Network (FV-PINN) with Reduced Derivative Order for Incompressible Flows This transformation facilitates the handling of complex bound- aries and discontinuities

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:35:12.268593Z

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Observation d044a503-ba87-46a4-a9ec-fecfb4c90156 · outbound

This paper cites Here, 𝑥 and 𝑦 are the horizontal and vertical coordinates of the flow field integration points.

Finite Volume Physical Informed Neural Network (FV-PINN) with Reduced Derivative Order for Incompressible Flows Here, 𝑥 and 𝑦 are the horizontal and vertical coordinates of the flow field integration points

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:35:12.253813Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 8de7ece1-b63c-4faa-b27c-334ba57e6ecd · outbound

This paper cites an unresolved cited work.

Finite Volume Physical Informed Neural Network (FV-PINN) with Reduced Derivative Order for Incompressible Flows Unresolved cited work

Reference 5

Resolution: unresolved; raw_fallback, observed 2026-08-12T12:35:12.237758Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 9dc6f1fd-b509-407c-b785-581e1152d63b · outbound

This paper cites an unresolved cited work.

Finite Volume Physical Informed Neural Network (FV-PINN) with Reduced Derivative Order for Incompressible Flows Unresolved cited work

Reference 6

Resolution: unresolved; raw_fallback, observed 2026-08-12T12:35:12.222707Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation fe0bc754-2242-44d1-b031-7bf040993b89 · outbound

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

Finite Volume Physical Informed Neural Network (FV-PINN) with Reduced Derivative Order for Incompressible Flows Physics -informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:35:12.208069Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 42dad436-891c-4044-83f1-cfce3fd36dbb · outbound

This paper cites Tackling the curse of dimensionality with physics -informed neural networks.

Finite Volume Physical Informed Neural Network (FV-PINN) with Reduced Derivative Order for Incompressible Flows Tackling the curse of dimensionality with physics -informed neural networks

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:35:12.193261Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 422049c4-fb53-45da-a185-beb0fa819cc8 · outbound

This paper cites A physics -informed variational DeepONet for predicting crack path in quasi-brittle materials.

Finite Volume Physical Informed Neural Network (FV-PINN) with Reduced Derivative Order for Incompressible Flows A physics -informed variational DeepONet for predicting crack path in quasi-brittle materials

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:35:12.176955Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 6db4bb13-8657-422a-945b-38d04be4b217 · outbound

This paper cites PINN Model of Diffusion Coefficient Identification Problem in Fick’s Laws.

Finite Volume Physical Informed Neural Network (FV-PINN) with Reduced Derivative Order for Incompressible Flows PINN Model of Diffusion Coefficient Identification Problem in Fick’s Laws

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:35:12.161898Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation e7880698-ddaa-4ae8-8a94-cc9b5f9994e9 · outbound

This paper cites Physics -informed Neural Networks (PINN) for computational solid mechanics: Numerical frameworks and applications.

Finite Volume Physical Informed Neural Network (FV-PINN) with Reduced Derivative Order for Incompressible Flows Physics -informed Neural Networks (PINN) for computational solid mechanics: Numerical frameworks and applications

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:35:12.132706Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation c9609ff5-703a-4771-a5bf-e1568348ef71 · outbound

This paper cites On physics-informed neural networks for quantum computers.

Finite Volume Physical Informed Neural Network (FV-PINN) with Reduced Derivative Order for Incompressible Flows On physics-informed neural networks for quantum computers

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:35:12.118139Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 6c96fbd6-7411-4404-8aed-0f6135799db9 · outbound

This paper cites Physics -informed deep learning for incompressible laminar flows.

Finite Volume Physical Informed Neural Network (FV-PINN) with Reduced Derivative Order for Incompressible Flows Physics -informed deep learning for incompressible laminar flows

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:35:12.103931Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 598acc3c-fcad-48df-9405-25b0b1a96a0d · outbound

This paper cites NSFnets (Navier -Stokes flow nets): Physics -informed neural networks for the incompressible Navier-Stokes equations.

Finite Volume Physical Informed Neural Network (FV-PINN) with Reduced Derivative Order for Incompressible Flows NSFnets (Navier -Stokes flow nets): Physics -informed neural networks for the incompressible Navier-Stokes equations

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:35:12.089377Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 6dbc516f-e333-4e69-9ee7-e2bfa408f8f7 · outbound

This paper cites Flow over an espresso cup: inferring 3 -D velocity and pressure fields from tomographic background oriented Schlieren via physics -informed neural networks.

Finite Volume Physical Informed Neural Network (FV-PINN) with Reduced Derivative Order for Incompressible Flows Flow over an espresso cup: inferring 3 -D velocity and pressure fields from tomographic background oriented Schlieren via physics -informed neural networks

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:35:12.074378Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 542fb157-7ef9-4d2d-916a-0d1364ac12de · outbound

This paper cites Physics-informed neural networks for high-speed flows.

Finite Volume Physical Informed Neural Network (FV-PINN) with Reduced Derivative Order for Incompressible Flows Physics-informed neural networks for high-speed flows

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:35:12.057756Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 79b486f1-7783-464f-bfb8-1e31dc405358 · outbound

This paper cites A physics -informed deep learning framework for inversion and surrogate modeling in solid mechanics.

Finite Volume Physical Informed Neural Network (FV-PINN) with Reduced Derivative Order for Incompressible Flows A physics -informed deep learning framework for inversion and surrogate modeling in solid mechanics

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:35:12.147299Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 472c3aaf-edf2-4ff6-a7b6-650e7fb086a9 · outbound

This paper cites Automatic differentiation in pytorch.

Finite Volume Physical Informed Neural Network (FV-PINN) with Reduced Derivative Order for Incompressible Flows Automatic differentiation in pytorch

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:35:12.041537Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 03cfad6a-9d83-4218-ba65-1e6e7d908632 · outbound

This paper cites DeepXDE: A deep learning library for solving differential equations.

Finite Volume Physical Informed Neural Network (FV-PINN) with Reduced Derivative Order for Incompressible Flows DeepXDE: A deep learning library for solving differential equations

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:35:12.026794Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 25dc72ad-8e56-4ae1-8c24-24625e2264e3 · outbound

This paper cites The deep Ritz method: a deep learning-based numerical algorithm for solving variational problems.

Finite Volume Physical Informed Neural Network (FV-PINN) with Reduced Derivative Order for Incompressible Flows The deep Ritz method: a deep learning-based numerical algorithm for solving variational problems

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:35:12.012362Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation e01b61d0-3953-4520-a454-529bddc96e5f · outbound

This paper cites Variational physics-informed neural networks for solving partial differential equations.

Finite Volume Physical Informed Neural Network (FV-PINN) with Reduced Derivative Order for Incompressible Flows Variational physics-informed neural networks for solving partial differential equations

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:35:11.993523Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 72480552-63c7-47dd-bf55-c8b01a6ca091 · outbound

This paper cites MIM: A deep mixed residual method for solving high -order partial differential equations.

Finite Volume Physical Informed Neural Network (FV-PINN) with Reduced Derivative Order for Incompressible Flows MIM: A deep mixed residual method for solving high -order partial differential equations

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:35:11.978539Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 15ef66a9-017f-437f-846f-fcdfc3011d4a · outbound

This paper cites wPINNs: Weak physics informed neural networks for approximating entropy solutions of hyperbolic conservation laws.

Finite Volume Physical Informed Neural Network (FV-PINN) with Reduced Derivative Order for Incompressible Flows wPINNs: Weak physics informed neural networks for approximating entropy solutions of hyperbolic conservation laws

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:35:11.962622Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 179b0ed1-5fd1-49e2-ac62-f7ad3a9948b0 · outbound

This paper cites Weak adversarial networks for high-dimensional partial differential equations.

Finite Volume Physical Informed Neural Network (FV-PINN) with Reduced Derivative Order for Incompressible Flows Weak adversarial networks for high-dimensional partial differential equations

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:35:11.946402Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation b686e40e-2aac-497a-9030-eadd152079c3 · outbound

This paper cites Mathematics of classical and quantum physics.

Finite Volume Physical Informed Neural Network (FV-PINN) with Reduced Derivative Order for Incompressible Flows Mathematics of classical and quantum physics

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:35:11.930451Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 828ba9c1-0372-4a1e-b5e9-5dfd473191cb · outbound

This paper cites Adam: A method for stochastic optimization.

Finite Volume Physical Informed Neural Network (FV-PINN) with Reduced Derivative Order for Incompressible Flows Adam: A method for stochastic optimization

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-12T12:35:11.799369Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 33fe17d7-dfed-4c01-9684-6a66ea7802cc · outbound

This paper cites On the limited memory BFGS method for large scale optimization.

Finite Volume Physical Informed Neural Network (FV-PINN) with Reduced Derivative Order for Incompressible Flows On the limited memory BFGS method for large scale optimization

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:35:11.904309Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 3bf14106-24eb-4c1b-8af3-dba979a3b8ec · outbound

This paper cites Numerical analysis.

Finite Volume Physical Informed Neural Network (FV-PINN) with Reduced Derivative Order for Incompressible Flows Numerical analysis

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:35:11.889059Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 0a9656c7-a0c9-4b86-b202-6080329293f3 · outbound

This paper cites Topology optimization of fluids in Stokes flow.

Finite Volume Physical Informed Neural Network (FV-PINN) with Reduced Derivative Order for Incompressible Flows Topology optimization of fluids in Stokes flow

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:35:11.873260Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 8d63cebe-14b4-46ca-a2b6-2cbf607d7c4d · outbound

This paper cites A detailed introduction to density -based topology optimisation of fluid flow problems with imple- mentation in MATLAB.

Finite Volume Physical Informed Neural Network (FV-PINN) with Reduced Derivative Order for Incompressible Flows A detailed introduction to density -based topology optimisation of fluid flow problems with imple- mentation in MATLAB

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:35:11.857072Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Pith citing papers

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