Paper Citation Record · LEDGER

Physics-Informed Neural Networks: Bridging the Divide Between Conservative and Non-Conservative Equations

As of 19 August 2026, this Paper Citation Record lists 53 of 53 outbound references and 1 inbound Pith citation observation for arXiv:2506.22413.

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

pith.paper-citation-record.v1
2506.22413 v3

Coverage vector

measured 53 of 53 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-25T08:13:36.224730Z

measured 54 of 54 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T19:21:59.619494Z

measured 1 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T02:28:24.338817Z

Reference resolution

53 of 53 outbound references displayed

verified exact4
verified fuzzy44
unresolved5
parse uncertain0
malformed identifier0
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External citation measurements

0
pith, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation 552ca0c1-fa3e-41b7-ae66-aed35580bb0c · outbound

This paper cites Three-level order-adaptive weighted essentially non-oscillatory schemes.

Physics-Informed Neural Networks: Bridging the Divide Between Conservative and Non-Conservative Equations Three-level order-adaptive weighted essentially non-oscillatory schemes

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T08:15:35.243751Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b9cebaf3-584f-4651-9f67-c94639914c67 · outbound

This paper cites Higher-order conservative discretizations on arbitrarily varying non-uniform grids.

Physics-Informed Neural Networks: Bridging the Divide Between Conservative and Non-Conservative Equations Higher-order conservative discretizations on arbitrarily varying non-uniform grids

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T08:15:35.247277Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7c2ae6a9-afee-4689-9934-b7464df2994c · outbound

This paper cites an unresolved cited work.

Physics-Informed Neural Networks: Bridging the Divide Between Conservative and Non-Conservative Equations Unresolved cited work

Reference 3

Resolution: unresolved; raw_fallback, observed 2026-05-25T08:15:35.235163Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0fad93d3-3c56-4a3a-aec1-ebfcf3946f4d · outbound

This paper cites Non-conservative and conservative formulations of characteristics-based numerical reconstructions for incompressible flows.

Physics-Informed Neural Networks: Bridging the Divide Between Conservative and Non-Conservative Equations Non-conservative and conservative formulations of characteristics-based numerical reconstructions for incompressible flows

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T08:15:35.232262Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0409b05f-8d53-4d21-93fb-780cd0726450 · outbound

This paper cites On the behavior of upwind schemes in the low mach number limit.

Physics-Informed Neural Networks: Bridging the Divide Between Conservative and Non-Conservative Equations On the behavior of upwind schemes in the low mach number limit

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T08:15:35.237656Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0bbc70ad-9cdd-4a18-859c-0d331f8d8988 · outbound

This paper cites Preconditioning techniques in computational fluid dynamics.

Physics-Informed Neural Networks: Bridging the Divide Between Conservative and Non-Conservative Equations Preconditioning techniques in computational fluid dynamics

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T08:15:35.225736Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1eeb30da-e1fb-4b75-8c97-398bcce59632 · outbound

This paper cites Conservative and non.

Physics-Informed Neural Networks: Bridging the Divide Between Conservative and Non-Conservative Equations Conservative and non

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T08:15:35.223003Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 46942891-3055-4545-9410-f2a5236286c5 · outbound

This paper cites A path-conservative ader discontinuous galerkin method for non-conservative hyperbolic systems: Applications to shallow water equations.

Physics-Informed Neural Networks: Bridging the Divide Between Conservative and Non-Conservative Equations A path-conservative ader discontinuous galerkin method for non-conservative hyperbolic systems: Applications to shallow water equations

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T08:15:35.228813Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 4e70892b-2255-4421-8d21-d57700f49fb3 · outbound

This paper cites an unresolved cited work.

Physics-Informed Neural Networks: Bridging the Divide Between Conservative and Non-Conservative Equations Unresolved cited work

Reference 9

Resolution: unresolved; raw_fallback, observed 2026-05-25T08:15:35.240587Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation dd4f77e3-be57-424e-8ef0-7a6d9f29a32a · outbound

This paper cites an unresolved cited work.

Physics-Informed Neural Networks: Bridging the Divide Between Conservative and Non-Conservative Equations Unresolved cited work

Reference 10

Resolution: unresolved; raw_fallback, observed 2026-05-25T08:15:35.249661Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 07677d74-5a3e-4913-9639-fba487a086cf · outbound

This paper cites an unresolved cited work.

Physics-Informed Neural Networks: Bridging the Divide Between Conservative and Non-Conservative Equations Unresolved cited work

Reference 11

Resolution: unresolved; raw_fallback, observed 2026-05-25T08:15:35.256140Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 31575dec-67a5-4d12-a7e2-8899453dbe26 · outbound

This paper cites an unresolved cited work.

Physics-Informed Neural Networks: Bridging the Divide Between Conservative and Non-Conservative Equations Unresolved cited work

Reference 12

Resolution: unresolved; raw_fallback, observed 2026-05-25T08:15:35.177267Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5d749276-0c3a-46f0-b868-d08d948d37c5 · outbound

This paper cites second order.

Physics-Informed Neural Networks: Bridging the Divide Between Conservative and Non-Conservative Equations second order

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T08:15:35.182672Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b41afc2d-c5b7-4498-ae8c-5cb14c47b225 · outbound

This paper cites Celia, Elias T.

Physics-Informed Neural Networks: Bridging the Divide Between Conservative and Non-Conservative Equations Celia, Elias T

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T08:15:35.191626Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9a52afb5-ede6-4e19-9b9a-9851ab75d6e9 · outbound

This paper cites Tanner and Ken Walters.

Physics-Informed Neural Networks: Bridging the Divide Between Conservative and Non-Conservative Equations Tanner and Ken Walters

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T08:15:35.206677Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 70ac1621-21b9-4095-ae9b-0d739f7dad76 · outbound

This paper cites Classical Mechanics.

Physics-Informed Neural Networks: Bridging the Divide Between Conservative and Non-Conservative Equations Classical Mechanics

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T08:15:35.200196Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9a08c9fc-5727-4b47-be5b-484c8e3631c2 · outbound

This paper cites Well-balanced high-order finite volume methods for systems of balance laws.

Physics-Informed Neural Networks: Bridging the Divide Between Conservative and Non-Conservative Equations Well-balanced high-order finite volume methods for systems of balance laws

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T08:15:35.150055Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 754d9b74-058d-4224-9a0a-e433685eaeb6 · outbound

This paper cites Definition and weak stability of nonconservative products.

Physics-Informed Neural Networks: Bridging the Divide Between Conservative and Non-Conservative Equations Definition and weak stability of nonconservative products

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T08:15:35.153471Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ab785e1e-99de-4563-ac71-207155f51d7b · outbound

This paper cites Accurate numerical discretizations of non-conservative hyperbolic systems.

Physics-Informed Neural Networks: Bridging the Divide Between Conservative and Non-Conservative Equations Accurate numerical discretizations of non-conservative hyperbolic systems

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T08:15:35.155999Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation fa1902b3-b01f-4913-9572-68ba7a9c8e1e · outbound

This paper cites Augmented roe schemes for nonconservative hyperbolic systems.

Physics-Informed Neural Networks: Bridging the Divide Between Conservative and Non-Conservative Equations Augmented roe schemes for nonconservative hyperbolic systems

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T08:15:35.146828Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a9f4737b-23a8-4fe0-8560-3058a11add16 · outbound

This paper cites Greenberg and A.Y.

Physics-Informed Neural Networks: Bridging the Divide Between Conservative and Non-Conservative Equations Greenberg and A.Y

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T08:15:35.135357Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 4ce1cd61-8d57-4676-b337-4ff925d54c88 · outbound

This paper cites Upstream differencing and godunov-type schemes for hyperbolic conservation laws.

Physics-Informed Neural Networks: Bridging the Divide Between Conservative and Non-Conservative Equations Upstream differencing and godunov-type schemes for hyperbolic conservation laws

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T08:15:35.140715Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 13282282-d9da-4163-8d94-bd9abb1b70cf · outbound

This paper cites Berger and Phillip Colella.

Physics-Informed Neural Networks: Bridging the Divide Between Conservative and Non-Conservative Equations Berger and Phillip Colella

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T08:15:35.143933Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 20e9da3b-96bc-4663-9ae5-e5949476434f · outbound

This paper cites A general technique for eliminating spurious oscillations in conservative schemes for multiphase and multispecies euler equations.

Physics-Informed Neural Networks: Bridging the Divide Between Conservative and Non-Conservative Equations A general technique for eliminating spurious oscillations in conservative schemes for multiphase and multispecies euler equations

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T08:15:35.165341Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8d290e23-90b4-439b-8bec-4abd08281eee · outbound

This paper cites A data-driven approach to predict artificial viscosity in high-order solvers.

Physics-Informed Neural Networks: Bridging the Divide Between Conservative and Non-Conservative Equations A data-driven approach to predict artificial viscosity in high-order solvers

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T08:15:35.174306Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 165ea2a0-d48f-4153-b7b0-e842d9257ab4 · outbound

This paper cites An optimal control deep learning method to design artificial viscosities for Discontinuous Galerkin schemes.

Physics-Informed Neural Networks: Bridging the Divide Between Conservative and Non-Conservative Equations An optimal control deep learning method to design artificial viscosities for Discontinuous Galerkin schemes

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-05-25T08:15:33.891955Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 33456d9a-a3b9-4afb-900a-585f2721d286 · outbound

This paper cites Physics-informed neural networks with adaptive localized artificial viscosity.

Physics-Informed Neural Networks: Bridging the Divide Between Conservative and Non-Conservative Equations Physics-informed neural networks with adaptive localized artificial viscosity

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T08:15:35.203597Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a4bcbc91-43aa-4ca0-aa2a-6b2670e5c9eb · outbound

This paper cites Physics-informed neural networks and higher- order high-resolution methods for resolving discontinuities and shocks: A comprehensive study.

Physics-Informed Neural Networks: Bridging the Divide Between Conservative and Non-Conservative Equations Physics-informed neural networks and higher- order high-resolution methods for resolving discontinuities and shocks: A comprehensive study

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T08:15:35.111530Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 54defb9c-8518-4242-9935-b9a9625758b7 · outbound

This paper cites Distributed physics informed neural network for data-efficient solution to partial differential equations.

Physics-Informed Neural Networks: Bridging the Divide Between Conservative and Non-Conservative Equations Distributed physics informed neural network for data-efficient solution to partial differential equations

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-05-25T08:15:33.886745Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation df00a92c-3ba0-4e3f-b962-0af904950680 · outbound

This paper cites Parallel physics-informed neural networks via domain decomposition.

Physics-Informed Neural Networks: Bridging the Divide Between Conservative and Non-Conservative Equations Parallel physics-informed neural networks via domain decomposition

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T08:15:35.252909Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation cebb7cff-81ad-4173-9c3f-89aaa495c7c0 · outbound

This paper cites Conservative physics-informed neural networks on discrete domains for conservation laws: Applications to forward and inverse problems.

Physics-Informed Neural Networks: Bridging the Divide Between Conservative and Non-Conservative Equations Conservative physics-informed neural networks on discrete domains for conservation laws: Applications to forward and inverse problems

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T08:15:35.269617Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 748d7d11-d9ab-4462-96f8-0da56332684d · 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.

Physics-Informed Neural Networks: Bridging the Divide Between Conservative and Non-Conservative Equations Extended physics-informed neural networks (xpinns): A generalized space-time domain decomposition based deep learning framework for nonlinear partial differential equations

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T08:15:35.212954Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b307d87c-cc1c-414c-a55a-9e0774c88ccd · outbound

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

Physics-Informed Neural Networks: Bridging the Divide Between Conservative and Non-Conservative Equations hp-vpinns: Variational physics-informed neural networks with domain decomposition

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T08:15:35.216447Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7d957efb-5503-4fdb-96c2-3be060aae134 · outbound

This paper cites Meshfree variational-physics-informed neural networks (mf-vpinn): An adaptive training strategy.

Physics-Informed Neural Networks: Bridging the Divide Between Conservative and Non-Conservative Equations Meshfree variational-physics-informed neural networks (mf-vpinn): An adaptive training strategy

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T08:15:35.219802Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 12e7ba42-ea7b-4f8b-98e0-3007539cd582 · outbound

This paper cites Integral pinns for hyperbolic conservation laws.

Physics-Informed Neural Networks: Bridging the Divide Between Conservative and Non-Conservative Equations Integral pinns for hyperbolic conservation laws

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T08:15:35.259781Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation af51c26a-834c-42a8-85a1-7cfcb9630fb5 · outbound

This paper cites Finite basis physics-informed neural networks (fbpinns): a scalable domain decomposition approach for solving differential equations.

Physics-Informed Neural Networks: Bridging the Divide Between Conservative and Non-Conservative Equations Finite basis physics-informed neural networks (fbpinns): a scalable domain decomposition approach for solving differential equations

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T08:15:35.171487Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ba78f953-206c-48c7-9769-831536afd144 · outbound

This paper cites Self-adaptive loss balanced physics-informed neural networks.

Physics-Informed Neural Networks: Bridging the Divide Between Conservative and Non-Conservative Equations Self-adaptive loss balanced physics-informed neural networks

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T08:15:35.124625Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2da54c74-20b3-46bb-bb01-fd665163f6b7 · outbound

This paper cites Variationally mimetic operator networks.

Physics-Informed Neural Networks: Bridging the Divide Between Conservative and Non-Conservative Equations Variationally mimetic operator networks

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T08:15:35.129268Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1d5e49fb-0502-4b85-951b-67e18082beb5 · outbound

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

Physics-Informed Neural Networks: Bridging the Divide Between Conservative and Non-Conservative Equations Learning nonlinear operators via deeponet based on the universal approximation theorem of operators

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T08:15:35.118583Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2ca871c5-40e8-43b3-8b52-321b1b562bde · outbound

This paper cites Physics-informed neural operator for learning partial differential equations.

Physics-Informed Neural Networks: Bridging the Divide Between Conservative and Non-Conservative Equations Physics-informed neural operator for learning partial differential equations

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T08:15:35.121741Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 4c27da72-27e1-4526-bf8a-ee8b0cd73ade · outbound

This paper cites Fourier Neural Operator for Parametric Partial Differential Equations.

Physics-Informed Neural Networks: Bridging the Divide Between Conservative and Non-Conservative Equations Fourier Neural Operator for Parametric Partial Differential Equations

Reference 41

Resolution: verified exact; local_arxiv, observed 2026-05-25T08:15:33.871205Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 93fc4b8c-b567-4ded-983f-7029a66d5da2 · outbound

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

Physics-Informed Neural Networks: Bridging the Divide Between Conservative and Non-Conservative Equations Physics-informed neural networks for high-speed flows

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T08:15:35.115128Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 62f8e27b-dc87-4e5e-9e6c-71e5d63d1ebd · outbound

This paper cites Learning shock waves in multiphase flows using physics-informed neural networks.

Physics-Informed Neural Networks: Bridging the Divide Between Conservative and Non-Conservative Equations Learning shock waves in multiphase flows using physics-informed neural networks

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T08:15:35.132531Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 4447102d-744e-47a7-9c06-f4470829b5eb · outbound

This paper cites Physics-Informed Neural Networks for Transonic Flows around an Airfoil.

Physics-Informed Neural Networks: Bridging the Divide Between Conservative and Non-Conservative Equations Physics-Informed Neural Networks for Transonic Flows around an Airfoil

Reference 44

Resolution: verified exact; arxiv_id, observed 2026-05-25T08:15:33.881088Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d5beb0da-686e-46d5-80cc-1c83158d68e8 · outbound

This paper cites Continuous and discontinuous compressible flows in a converging–diverging channel solved by physics-informed neural networks without exogenous data.

Physics-Informed Neural Networks: Bridging the Divide Between Conservative and Non-Conservative Equations Continuous and discontinuous compressible flows in a converging–diverging channel solved by physics-informed neural networks without exogenous data

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T08:15:35.209793Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1a0133eb-b282-4cc7-b174-787234a4b6ab · outbound

This paper cites Physics informed neural networks for fluid flow analysis with repetitive parameter initialization.

Physics-Informed Neural Networks: Bridging the Divide Between Conservative and Non-Conservative Equations Physics informed neural networks for fluid flow analysis with repetitive parameter initialization

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T08:15:35.185567Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e926bb8c-870d-4dde-ac5c-f5615ffff459 · outbound

This paper cites Learning an optimised stable taylor-galerkin convection scheme based on a local spectral model for the numerical error dynamics.

Physics-Informed Neural Networks: Bridging the Divide Between Conservative and Non-Conservative Equations Learning an optimised stable taylor-galerkin convection scheme based on a local spectral model for the numerical error dynamics

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T08:15:35.263157Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5abcb7b8-fad5-4efc-b144-4c55d3781c98 · outbound

This paper cites Learning numerical viscosity using artificial neural regression network.

Physics-Informed Neural Networks: Bridging the Divide Between Conservative and Non-Conservative Equations Learning numerical viscosity using artificial neural regression network

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T08:15:35.266347Z

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Observation 0f47d878-e64d-48d2-a245-b0cdfc136d17 · outbound

This paper cites An efficient three-level weighted essentially non-oscillatory scheme for hyperbolic equations.

Physics-Informed Neural Networks: Bridging the Divide Between Conservative and Non-Conservative Equations An efficient three-level weighted essentially non-oscillatory scheme for hyperbolic equations

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T08:15:35.180120Z

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Observation a57842d8-7f46-473d-9b69-90b0bff4918b · outbound

This paper cites Higher-order slope limiters for euler equation.

Physics-Informed Neural Networks: Bridging the Divide Between Conservative and Non-Conservative Equations Higher-order slope limiters for euler equation

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T08:15:35.188807Z

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Observation 8dd14aba-b1ac-414f-b30d-39e24f10c507 · outbound

This paper cites Hyperbolic runge–kutta method using evolutionary algorithm.

Physics-Informed Neural Networks: Bridging the Divide Between Conservative and Non-Conservative Equations Hyperbolic runge–kutta method using evolutionary algorithm

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T08:15:35.194603Z

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Observation 73a5c28c-7d6b-43f1-b944-479c1b670b8f · outbound

This paper cites Numerical solution of the euler equations by finite volume methods using runge kutta time stepping schemes.

Physics-Informed Neural Networks: Bridging the Divide Between Conservative and Non-Conservative Equations Numerical solution of the euler equations by finite volume methods using runge kutta time stepping schemes

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T08:15:35.197570Z

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Observation a1f04cc1-53c2-4a66-8f72-008b0fa5db1d · outbound

This paper cites Discontinuity computing using physics-informed neural networks.

Physics-Informed Neural Networks: Bridging the Divide Between Conservative and Non-Conservative Equations Discontinuity computing using physics-informed neural networks

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T08:15:35.168222Z

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Pith citing papers

Observation 29c6ce57-8f32-4045-a237-6823d31efed7 · inbound

Variational quantum algorithm for anion exchange across electrolyzer membrane cites this paper.

Variational quantum algorithm for anion exchange across electrolyzer membrane Physics-Informed Neural Networks: Bridging the Divide Between Conservative and Non-Conservative Equations

Reference 49

Resolution: verified exact; local_arxiv, observed 2026-08-03T19:23:30.333646Z

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