Paper Citation Record · LEDGER

Inverse Low-Dimensional Manifold Reconstruction Framework for Spatiotemporal Reconstruction of Compressible Physical Fields

As of 10 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 0 inbound Pith citation observations for arXiv:2607.07084.

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

pith.paper-citation-record.v1
2607.07084 v1

Coverage vector

measured 32 of 32 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-07-09T20:27:11.655674Z

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+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

32 of 32 outbound references displayed

verified exact3
verified fuzzy29
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7116b0b8-f16c-4fac-9600-fe4d53ccfb2b · outbound

This paper cites A multiphase model for compressible flows with interfaces, shocks, detonation waves and cavitation.

Inverse Low-Dimensional Manifold Reconstruction Framework for Spatiotemporal Reconstruction of Compressible Physical Fields A multiphase model for compressible flows with interfaces, shocks, detonation waves and cavitation

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T20:36:31.739789Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 94733055-e76b-41ad-995d-4f2cc04aae18 · outbound

This paper cites Discovering governing equations from data by sparse identification of nonlinear dynamical systems.

Inverse Low-Dimensional Manifold Reconstruction Framework for Spatiotemporal Reconstruction of Compressible Physical Fields Discovering governing equations from data by sparse identification of nonlinear dynamical systems

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T20:36:31.753445Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 5493d9b2-58ac-4c8a-8b06-81e33bffd87c · outbound

This paper cites Data-driven discovery of partial differential equations.

Inverse Low-Dimensional Manifold Reconstruction Framework for Spatiotemporal Reconstruction of Compressible Physical Fields Data-driven discovery of partial differential equations

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T20:36:31.734621Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ea43d5f1-66b5-4d34-8e46-f7d57b882ef0 · outbound

This paper cites Measurements of weak and moderate oblique shock- vortex interactions in supersonic flow.

Inverse Low-Dimensional Manifold Reconstruction Framework for Spatiotemporal Reconstruction of Compressible Physical Fields Measurements of weak and moderate oblique shock- vortex interactions in supersonic flow

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T20:36:31.727841Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation cc269969-5afb-4009-9658-5c25d4bc92fe · outbound

This paper cites Novel spectral methods for shock capturing and the removal of tygers in computational fluid dynamics.

Inverse Low-Dimensional Manifold Reconstruction Framework for Spatiotemporal Reconstruction of Compressible Physical Fields Novel spectral methods for shock capturing and the removal of tygers in computational fluid dynamics

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T20:36:31.740141Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3e4cc35c-3702-4e9f-805d-8fa3ffbef748 · outbound

This paper cites Nonlinear Reconstruction for Operator Learning of PDEs with Discontinuities.

Inverse Low-Dimensional Manifold Reconstruction Framework for Spatiotemporal Reconstruction of Compressible Physical Fields Nonlinear Reconstruction for Operator Learning of PDEs with Discontinuities

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-07-09T20:36:31.520397Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation dd717c84-4ee1-4cd0-9cb0-b10e08ccdcbd · outbound

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

Inverse Low-Dimensional Manifold Reconstruction Framework for Spatiotemporal Reconstruction of Compressible Physical Fields Fourier Neural Operator for Parametric Partial Differential Equations

Reference 7

Resolution: verified exact; local_arxiv, observed 2026-07-09T20:36:31.517801Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b9c17bf2-fbb9-4486-a762-657f4bf0b5e9 · outbound

This paper cites A neural network based shock detection and localization approach for non-differentiable Galerkin methods.

Inverse Low-Dimensional Manifold Reconstruction Framework for Spatiotemporal Reconstruction of Compressible Physical Fields A neural network based shock detection and localization approach for non-differentiable Galerkin methods

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T20:36:31.751454Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4843b38b-4d79-4cb0-9bde-637f8d0d71e0 · outbound

This paper cites Using deep neural networks for detecting spurious oscillations in non-differentiable Galerkin solutions of convection-dominated convection–diffusion equations.

Inverse Low-Dimensional Manifold Reconstruction Framework for Spatiotemporal Reconstruction of Compressible Physical Fields Using deep neural networks for detecting spurious oscillations in non-differentiable Galerkin solutions of convection-dominated convection–diffusion equations

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T20:36:31.724900Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 5fec3403-656a-4723-8cc9-7bb2250e973e · outbound

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

Inverse Low-Dimensional Manifold Reconstruction Framework for Spatiotemporal Reconstruction of Compressible Physical Fields Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T20:36:31.749609Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 10575c48-4cdc-40f3-93f4-d747171ffcc3 · outbound

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

Inverse Low-Dimensional Manifold Reconstruction Framework for Spatiotemporal Reconstruction of Compressible Physical Fields Conservative physics-informed neural networks on discrete domains for conservation laws: Applications to forward and inverse problems

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T20:36:31.722947Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 64108166-daae-4a5d-b03f-567e25857eee · outbound

This paper cites LT-PINN: Lagrangian topology-conscious physics- informed neural network for boundary -focused engineering optimization.

Inverse Low-Dimensional Manifold Reconstruction Framework for Spatiotemporal Reconstruction of Compressible Physical Fields LT-PINN: Lagrangian topology-conscious physics- informed neural network for boundary -focused engineering optimization

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T20:36:31.736166Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 06faefdc-9237-43f3-8435-f375cab87cf6 · outbound

This paper cites Coupled pressure and saturation prediction for two - phase flow in porous media using physics-informed neural networks (PINNs).

Inverse Low-Dimensional Manifold Reconstruction Framework for Spatiotemporal Reconstruction of Compressible Physical Fields Coupled pressure and saturation prediction for two - phase flow in porous media using physics-informed neural networks (PINNs)

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T20:36:31.732733Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6b4dbcc5-0b99-4b9d-a6e7-c954178df7cc · outbound

This paper cites Physics-informed neural networks for cardiac activation mapping.

Inverse Low-Dimensional Manifold Reconstruction Framework for Spatiotemporal Reconstruction of Compressible Physical Fields Physics-informed neural networks for cardiac activation mapping

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T20:36:31.750576Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 065be165-056f-4c20-bc1a-03cf9df70276 · outbound

This paper cites A hybrid model and data driven approach for ballistic prediction with PINN.

Inverse Low-Dimensional Manifold Reconstruction Framework for Spatiotemporal Reconstruction of Compressible Physical Fields A hybrid model and data driven approach for ballistic prediction with PINN

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T20:36:31.718701Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 069b3e46-545a-496c-90b2-aea1624c067a · outbound

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

Inverse Low-Dimensional Manifold Reconstruction Framework for Spatiotemporal Reconstruction of Compressible Physical Fields Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T20:36:31.746861Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e28403f2-8661-4234-9cb4-dd63fde6d2a1 · outbound

This paper cites Riemannonets: Interpretable neural operators for riemann problems.

Inverse Low-Dimensional Manifold Reconstruction Framework for Spatiotemporal Reconstruction of Compressible Physical Fields Riemannonets: Interpretable neural operators for riemann problems

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T20:36:31.730134Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 0a579601-dbd8-415e-9c44-ce067d3a2e51 · outbound

This paper cites R-adaptive DeepONet: Learning Solution Operators for PDEs with Discontinuous Solutions Using an R-adaptive Strategy.

Inverse Low-Dimensional Manifold Reconstruction Framework for Spatiotemporal Reconstruction of Compressible Physical Fields R-adaptive DeepONet: Learning Solution Operators for PDEs with Discontinuous Solutions Using an R-adaptive Strategy

Reference 18

Resolution: verified exact; local_arxiv, observed 2026-07-09T20:36:31.514706Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 38ae51b6-efaf-4449-87cb-0d4884ad5809 · outbound

This paper cites Sequential deep operator networks (s-deeponet) for predicting full-field solutions under time-dependent loads.

Inverse Low-Dimensional Manifold Reconstruction Framework for Spatiotemporal Reconstruction of Compressible Physical Fields Sequential deep operator networks (s-deeponet) for predicting full-field solutions under time-dependent loads

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T20:36:31.716493Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation fe57d28c-b4ef-47f6-b4e5-2f3511989a43 · outbound

This paper cites Deep transfer operator learning for partial differential equations under conditional shift.

Inverse Low-Dimensional Manifold Reconstruction Framework for Spatiotemporal Reconstruction of Compressible Physical Fields Deep transfer operator learning for partial differential equations under conditional shift

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T20:36:31.725635Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7aca142d-4dc5-499a-9ee1-6a559c31da1c · outbound

This paper cites Enhanced fifth order WENO shock-capturing schemes with deep learning.

Inverse Low-Dimensional Manifold Reconstruction Framework for Spatiotemporal Reconstruction of Compressible Physical Fields Enhanced fifth order WENO shock-capturing schemes with deep learning

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T20:36:31.732394Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation fe9cc8e1-0b9e-4f86-ba98-152fc5372450 · outbound

This paper cites Machine learning-based WENO5 scheme.

Inverse Low-Dimensional Manifold Reconstruction Framework for Spatiotemporal Reconstruction of Compressible Physical Fields Machine learning-based WENO5 scheme

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T20:36:31.745826Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 1b5b5e8d-39ef-48ba-bf6a-943ba8479cd3 · outbound

This paper cites A data -driven shock capturing approach for non-differentiable Galekin methods.

Inverse Low-Dimensional Manifold Reconstruction Framework for Spatiotemporal Reconstruction of Compressible Physical Fields A data -driven shock capturing approach for non-differentiable Galekin methods

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T20:36:31.752311Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 266d703c-1198-48c2-a5b8-fe661f281e42 · outbound

This paper cites WCNS3-MR-NN: A machine learning-based shock- capturing scheme with accuracy-preserving and high -resolution properties.

Inverse Low-Dimensional Manifold Reconstruction Framework for Spatiotemporal Reconstruction of Compressible Physical Fields WCNS3-MR-NN: A machine learning-based shock- capturing scheme with accuracy-preserving and high -resolution properties

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T20:36:31.728795Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 37cea42e-61d5-4933-a6e9-2258f5e5ee01 · outbound

This paper cites Enhancement of shock-capturing methods via machine learning.

Inverse Low-Dimensional Manifold Reconstruction Framework for Spatiotemporal Reconstruction of Compressible Physical Fields Enhancement of shock-capturing methods via machine learning

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T20:36:31.738320Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 46ae95ca-712e-407f-a8b9-54b22296b0cf · outbound

This paper cites Grasping extreme aerodynamics on a low- dimensional manifold.

Inverse Low-Dimensional Manifold Reconstruction Framework for Spatiotemporal Reconstruction of Compressible Physical Fields Grasping extreme aerodynamics on a low- dimensional manifold

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T20:36:31.745136Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 298856c2-11a3-4a45-8c2c-f8d3aba4a6f2 · outbound

This paper cites Manifold learning: What, how, and why.

Inverse Low-Dimensional Manifold Reconstruction Framework for Spatiotemporal Reconstruction of Compressible Physical Fields Manifold learning: What, how, and why

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T20:36:31.743287Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b4c2a80d-0775-41cf-a691-9ac62e81d798 · outbound

This paper cites Extrapolated Shock Tracking: bridging shock-fitting and embedded boundary methods.

Inverse Low-Dimensional Manifold Reconstruction Framework for Spatiotemporal Reconstruction of Compressible Physical Fields Extrapolated Shock Tracking: bridging shock-fitting and embedded boundary methods

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T20:36:31.736460Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 8fe55b73-df91-496e-9739-8d2e6916e801 · outbound

This paper cites Manifold learning-based reduced -order model for full speed flow field.

Inverse Low-Dimensional Manifold Reconstruction Framework for Spatiotemporal Reconstruction of Compressible Physical Fields Manifold learning-based reduced -order model for full speed flow field

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T20:36:31.741633Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 02a6f59e-89b2-45f1-9f6d-495cdef769f1 · outbound

This paper cites The proper orthogonal decomposition in the analysis of turbulent flows.

Inverse Low-Dimensional Manifold Reconstruction Framework for Spatiotemporal Reconstruction of Compressible Physical Fields The proper orthogonal decomposition in the analysis of turbulent flows

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T20:36:31.747722Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3f30649b-79b6-463d-9a4c-c56163e9a297 · outbound

This paper cites Fifth-order A-WENO schemes based on the path-conservative central-upwind method.

Inverse Low-Dimensional Manifold Reconstruction Framework for Spatiotemporal Reconstruction of Compressible Physical Fields Fifth-order A-WENO schemes based on the path-conservative central-upwind method

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T20:36:31.741939Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ee45a918-0cc0-4563-a27a-c71acfcf9e65 · outbound

This paper cites An Eulerian SPH method with WENO reconstruction for compressible and incompressible flows.

Inverse Low-Dimensional Manifold Reconstruction Framework for Spatiotemporal Reconstruction of Compressible Physical Fields An Eulerian SPH method with WENO reconstruction for compressible and incompressible flows

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T20:36:31.748811Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Pith citing papers

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