Paper Citation Record · LEDGER

Dual-Correction Physics-Informed Neural Networks for Hemodynamic Reconstruction from Sparse Data

As of 6 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 0 inbound Pith citation observations for arXiv:2605.12544.

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

pith.paper-citation-record.v1
2605.12544 v1

Coverage vector

measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-14T21:58:44.404372Z

measured 40 of 40 standing notices

One-hop event checks from named stored sources.

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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

40 of 40 outbound references displayed

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verified fuzzy36
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9cc7f1ad-4951-426d-a178-95a533d1a19e · outbound

This paper cites A review of 3D vessel lumen segmentation techniques: models, features and extraction schemes.

Dual-Correction Physics-Informed Neural Networks for Hemodynamic Reconstruction from Sparse Data A review of 3D vessel lumen segmentation techniques: models, features and extraction schemes

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T21:59:30.710014Z

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Observation 61eec8fc-3df3-415c-8e8a-17c0431ab428 · outbound

This paper cites Blood vessel segmentation algorithms—Review of methods, datasets and evaluation metrics.

Dual-Correction Physics-Informed Neural Networks for Hemodynamic Reconstruction from Sparse Data Blood vessel segmentation algorithms—Review of methods, datasets and evaluation metrics

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T21:59:30.717147Z

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 0324a83d-8f73-4fa0-96fd-b17c52d55179 · outbound

This paper cites Fluid-structure interaction analysis of pulsatile flow in arterial aneurysms with physics-informed neural networks and computational fluid dynamics.

Dual-Correction Physics-Informed Neural Networks for Hemodynamic Reconstruction from Sparse Data Fluid-structure interaction analysis of pulsatile flow in arterial aneurysms with physics-informed neural networks and computational fluid dynamics

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T21:59:30.696857Z

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Observation 706f859d-f451-458d-8acf-bcee07b46c37 · outbound

This paper cites Automated generation of 0D and 1D reduced-order models of patient-specific blood flow.

Dual-Correction Physics-Informed Neural Networks for Hemodynamic Reconstruction from Sparse Data Automated generation of 0D and 1D reduced-order models of patient-specific blood flow

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T21:59:30.739866Z

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Observation cd55cb3f-c393-419f-8efc-5019ac6ff58c · outbound

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

Dual-Correction Physics-Informed Neural Networks for Hemodynamic Reconstruction from Sparse Data Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T21:59:30.822649Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 40b6e635-600b-4d71-bb6b-45c9409a86b4 · outbound

This paper cites Physics-Informed Neural Networks for Brain Hemodynamic Predictions Using Medical Imaging.

Dual-Correction Physics-Informed Neural Networks for Hemodynamic Reconstruction from Sparse Data Physics-Informed Neural Networks for Brain Hemodynamic Predictions Using Medical Imaging

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T21:59:30.792257Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 21915b14-9e5b-4efd-968c-87b8dc8c87f3 · outbound

This paper cites Flow-Rate-Constrained Physics-Informed Neural Net- works for Flow Field Error Correction in Four-Dimensional Flow Magnetic Resonance Imaging.

Dual-Correction Physics-Informed Neural Networks for Hemodynamic Reconstruction from Sparse Data Flow-Rate-Constrained Physics-Informed Neural Net- works for Flow Field Error Correction in Four-Dimensional Flow Magnetic Resonance Imaging

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T21:59:30.771199Z

Source-reported events for the cited work

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Observation 4c0ccaf3-0d19-45eb-aa83-61f65629b2c7 · outbound

This paper cites Physics-Informed Graph Neural Networks to solve 1-D equations of blood flow.

Dual-Correction Physics-Informed Neural Networks for Hemodynamic Reconstruction from Sparse Data Physics-Informed Graph Neural Networks to solve 1-D equations of blood flow

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T21:59:30.818761Z

Source-reported events for the cited work

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Observation 67395cf1-81ac-4066-a128-d447280d0a7f · outbound

This paper cites Towards Physics-informed Deep Learning for Turbulent Flow Prediction.

Dual-Correction Physics-Informed Neural Networks for Hemodynamic Reconstruction from Sparse Data Towards Physics-informed Deep Learning for Turbulent Flow Prediction

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-14T21:59:30.043425Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 74e371ae-7bfe-4316-a963-98df28a2f0b1 · outbound

This paper cites Automatic differentiation in machine learning: a survey.

Dual-Correction Physics-Informed Neural Networks for Hemodynamic Reconstruction from Sparse Data Automatic differentiation in machine learning: a survey

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T21:59:30.814655Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 695c4522-f755-4d4c-965a-1f3a691ccd7e · outbound

This paper cites ICPINN: Integral conservation physics-informed neural networks based on adaptive activation functions for 3D blood flow simulations.

Dual-Correction Physics-Informed Neural Networks for Hemodynamic Reconstruction from Sparse Data ICPINN: Integral conservation physics-informed neural networks based on adaptive activation functions for 3D blood flow simulations

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T21:59:30.810657Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 3a57e25c-dd11-4fb4-bff8-13c4e72e1bea · outbound

This paper cites Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems.

Dual-Correction Physics-Informed Neural Networks for Hemodynamic Reconstruction from Sparse Data Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T21:59:30.800948Z

Source-reported events for the cited work

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Observation 155dec15-1d99-46d2-a6fb-8809100053d6 · outbound

This paper cites Performance of Fourier-based activation function in physics-informed neural networks for patient-specific car- diovascular flows.

Dual-Correction Physics-Informed Neural Networks for Hemodynamic Reconstruction from Sparse Data Performance of Fourier-based activation function in physics-informed neural networks for patient-specific car- diovascular flows

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T21:59:30.805977Z

Source-reported events for the cited work

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Observation 1708a8ea-932c-4fc2-a5a7-b3691845ef75 · outbound

This paper cites PALQO: Physics-informed Model for Accelerating Large-scale Quantum Optimization.

Dual-Correction Physics-Informed Neural Networks for Hemodynamic Reconstruction from Sparse Data PALQO: Physics-informed Model for Accelerating Large-scale Quantum Optimization

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-14T21:59:30.040405Z

Source-reported events for the cited work

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Observation 24b00789-3471-418e-a749-9f37c245cbb5 · outbound

This paper cites Quantum Physics-Informed Neural Networks.

Dual-Correction Physics-Informed Neural Networks for Hemodynamic Reconstruction from Sparse Data Quantum Physics-Informed Neural Networks

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T21:59:30.705966Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 320219e6-47d5-4d4c-971f-b293f06098ec · outbound

This paper cites Hybrid quantum physics-informed neural networks for simulating computational fluid dynamics in complex shapes.

Dual-Correction Physics-Informed Neural Networks for Hemodynamic Reconstruction from Sparse Data Hybrid quantum physics-informed neural networks for simulating computational fluid dynamics in complex shapes

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T21:59:30.721177Z

Source-reported events for the cited work

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Observation 728dfe4b-d172-4adc-9561-1682bd15086a · outbound

This paper cites Self-adaptive physics- informed quantum machine learning for solving differential equations.

Dual-Correction Physics-Informed Neural Networks for Hemodynamic Reconstruction from Sparse Data Self-adaptive physics- informed quantum machine learning for solving differential equations

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T21:59:30.755500Z

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Observation 61a13ed9-7755-452e-9a75-44c3030f7da5 · outbound

This paper cites Understanding and Mitigating Gradient Flow Pathologies in Physics-Informed Neural Networks.

Dual-Correction Physics-Informed Neural Networks for Hemodynamic Reconstruction from Sparse Data Understanding and Mitigating Gradient Flow Pathologies in Physics-Informed Neural Networks

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T21:59:30.830156Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation bef6fc53-8d34-49ad-a52f-18b646fc58f6 · outbound

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

Dual-Correction Physics-Informed Neural Networks for Hemodynamic Reconstruction from Sparse Data Self-adaptive physics-informed neural networks

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T21:59:30.781643Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation bc74abdb-4aca-4fd7-9fe4-fb44f4a158ed · outbound

This paper cites A composite neural network that learns from multi-fidelity data: Application to function approximation and inverse PDE problems.

Dual-Correction Physics-Informed Neural Networks for Hemodynamic Reconstruction from Sparse Data A composite neural network that learns from multi-fidelity data: Application to function approximation and inverse PDE problems

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T21:59:30.834088Z

Source-reported events for the cited work

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Observation 76f3c4f7-ad36-4b62-844c-e2293a8ed7c3 · outbound

This paper cites ADMM-Net: A Deep Learning Approach for Compres- sive Sensing MRI.

Dual-Correction Physics-Informed Neural Networks for Hemodynamic Reconstruction from Sparse Data ADMM-Net: A Deep Learning Approach for Compres- sive Sensing MRI

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T21:59:30.784054Z

Source-reported events for the cited work

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Observation 7f13cd3d-b8a2-41e0-af9c-530917b0809f · outbound

This paper cites Deep Residual Learning for Image Recognition: A Survey.

Dual-Correction Physics-Informed Neural Networks for Hemodynamic Reconstruction from Sparse Data Deep Residual Learning for Image Recognition: A Survey

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T21:59:30.767028Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation a003701c-0960-4496-94bf-767d2291b33a · outbound

This paper cites Active learning using transductive sparse Bayesian regression.

Dual-Correction Physics-Informed Neural Networks for Hemodynamic Reconstruction from Sparse Data Active learning using transductive sparse Bayesian regression

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T21:59:30.779335Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 40705887-56ab-46bb-8c89-c1ea394a8380 · outbound

This paper cites MR Image Reconstruction Using Deep Density Priors.

Dual-Correction Physics-Informed Neural Networks for Hemodynamic Reconstruction from Sparse Data MR Image Reconstruction Using Deep Density Priors

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T21:59:30.842521Z

Source-reported events for the cited work

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Observation db1eb016-e978-4604-80bf-b43e6aa5bc6f · outbound

This paper cites Physics-informed Score-based Diffusion Model for Limited-angle Reconstruction of Cardiac Computed Tomography.

Dual-Correction Physics-Informed Neural Networks for Hemodynamic Reconstruction from Sparse Data Physics-informed Score-based Diffusion Model for Limited-angle Reconstruction of Cardiac Computed Tomography

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T21:59:30.826289Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation b889ba5a-747e-4bfb-8b16-9698a2ce1a7c · outbound

This paper cites Depth-aware guidance with self-estimated depth repre- sentations of diffusion models.

Dual-Correction Physics-Informed Neural Networks for Hemodynamic Reconstruction from Sparse Data Depth-aware guidance with self-estimated depth repre- sentations of diffusion models

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T21:59:30.775898Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation d9e63783-35b1-4b91-8955-8b5daf1aa00c · outbound

This paper cites Score-Based Generative Modeling through Stochastic Differential Equations.

Dual-Correction Physics-Informed Neural Networks for Hemodynamic Reconstruction from Sparse Data Score-Based Generative Modeling through Stochastic Differential Equations

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T21:59:30.736331Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 9b2159a4-4273-4b4d-93a8-40e25f6b937e · outbound

This paper cites PyTorch: An Imperative Style, High-Performance Deep Learning Library.

Dual-Correction Physics-Informed Neural Networks for Hemodynamic Reconstruction from Sparse Data PyTorch: An Imperative Style, High-Performance Deep Learning Library

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T21:59:30.765991Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation e8b59791-2737-4660-8da5-b39a0bf38fd3 · outbound

This paper cites 3DVascNet: an automated software for segmentation and quantification of vascular networks in 3D.

Dual-Correction Physics-Informed Neural Networks for Hemodynamic Reconstruction from Sparse Data 3DVascNet: an automated software for segmentation and quantification of vascular networks in 3D

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T21:59:30.750823Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation e26b406b-98af-48a3-9db6-36b2cadec765 · outbound

This paper cites VesselSAM: Leveraging SAM for Aortic Vessel Segmentation with AtrousLoRA.

Dual-Correction Physics-Informed Neural Networks for Hemodynamic Reconstruction from Sparse Data VesselSAM: Leveraging SAM for Aortic Vessel Segmentation with AtrousLoRA

Reference 30

Resolution: verified exact; arxiv_id, observed 2026-05-14T21:59:30.027696Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 6cf9202b-16ab-4622-90da-f675bbab5f70 · outbound

This paper cites A deep learning approach to automate high- resolution blood vessel reconstruction on computerised tomography images with or without the use of contrast agents.

Dual-Correction Physics-Informed Neural Networks for Hemodynamic Reconstruction from Sparse Data A deep learning approach to automate high- resolution blood vessel reconstruction on computerised tomography images with or without the use of contrast agents

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T21:59:30.763033Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 512d2f4b-1944-498f-a96e-a081674e3c30 · outbound

This paper cites Coarse-to-fine multiplanar D-SEA UNet for automatic 3D carotid segmentation in CTA images.

Dual-Correction Physics-Informed Neural Networks for Hemodynamic Reconstruction from Sparse Data Coarse-to-fine multiplanar D-SEA UNet for automatic 3D carotid segmentation in CTA images

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T21:59:30.748617Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation a5c518aa-be66-48d2-a356-2aa02612cf6f · outbound

This paper cites Deep Open Snake Tracker for Vessel Tracing.

Dual-Correction Physics-Informed Neural Networks for Hemodynamic Reconstruction from Sparse Data Deep Open Snake Tracker for Vessel Tracing

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-05-14T21:59:30.030567Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation ac4ef9bf-1b3e-4e4a-9728-86b19fe4c79e · outbound

This paper cites Super-resolution 4D flow MRI to quantify aortic regurgitation using computational fluid dynamics and deep learning.

Dual-Correction Physics-Informed Neural Networks for Hemodynamic Reconstruction from Sparse Data Super-resolution 4D flow MRI to quantify aortic regurgitation using computational fluid dynamics and deep learning

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T21:59:30.787654Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation dfde0e9c-0d79-417c-8dc8-bdf1058f3107 · outbound

This paper cites Virtual injections using 4D flow MRI with dis- placement corrections and constrained probabilistic streamlines.

Dual-Correction Physics-Informed Neural Networks for Hemodynamic Reconstruction from Sparse Data Virtual injections using 4D flow MRI with dis- placement corrections and constrained probabilistic streamlines

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T21:59:30.738395Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 8c8e46f9-578e-4cbc-aecb-cc807c44db83 · outbound

This paper cites Physics-informed neural networks for modeling physi- ological time series for cuffless blood pressure estimation.

Dual-Correction Physics-Informed Neural Networks for Hemodynamic Reconstruction from Sparse Data Physics-informed neural networks for modeling physi- ological time series for cuffless blood pressure estimation

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T21:59:30.744121Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 94859282-6ec6-4d53-8cf4-e61d2e9db2bd · outbound

This paper cites Generative adversarial dehaze mapping nets.

Dual-Correction Physics-Informed Neural Networks for Hemodynamic Reconstruction from Sparse Data Generative adversarial dehaze mapping nets

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T21:59:30.838755Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 8e083dfd-0af6-4de3-b5de-4241f13b9187 · outbound

This paper cites Magnetic Resonance Electrical Properties Tomography Based on Modified Physics-Informed Neural Network and Multicon- straints.

Dual-Correction Physics-Informed Neural Networks for Hemodynamic Reconstruction from Sparse Data Magnetic Resonance Electrical Properties Tomography Based on Modified Physics-Informed Neural Network and Multicon- straints

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T21:59:30.796373Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 2f683944-e7e7-4416-8c0b-7646392a05e6 · outbound

This paper cites Physics-Informed DeepMRI: k-Space Interpolation Meets Heat Diffusion.

Dual-Correction Physics-Informed Neural Networks for Hemodynamic Reconstruction from Sparse Data Physics-Informed DeepMRI: k-Space Interpolation Meets Heat Diffusion

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T21:59:30.852366Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 20d4015a-da0f-4c64-a286-997e285ea80d · outbound

This paper cites Enhancing Brain Source Reconstruction by Initializing 3D Neural Networks with Physical Inverse Solutions.

Dual-Correction Physics-Informed Neural Networks for Hemodynamic Reconstruction from Sparse Data Enhancing Brain Source Reconstruction by Initializing 3D Neural Networks with Physical Inverse Solutions

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T21:59:30.846779Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Pith citing papers

No inbound Pith citation observations are available.