Pith Number

pith:JRKJCT4Z

pith:2026:JRKJCT4ZTE6TH4V66DSLGFNDHS

not attested not anchored not stored refs resolved

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

Hao Wu, Jingtai Song, Qinsheng Zhu, Xianwen Zhang, Xiaodong Xing, Yufeng Tang, Zhiyun Zhang

A dual-correction physics-informed neural network reconstructs accurate blood flow fields in tortuous intracranial arteries from sparse data.

arxiv:2605.12544 v1 · 2026-05-09 · physics.med-ph

Open paper page JSON Open Graph Bundle Merged state Verified badge What is a Pith Number?

Add to your LaTeX paper

\usepackage{pith}
\pithnumber{JRKJCT4ZTE6TH4V66DSLGFNDHS}

Prints a linked badge after your title and injects PDF metadata. Compiles on arXiv. Learn more · Embed verified badge

Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

The proposed DCP-INN model utilizes a diamond-shaped main network to capture low-frequency trends in physical evolution, and employs a parallel wide-deep correction network to compensate for high-frequency residuals resulting from complex geometric shapes... The results demonstrate that the method effectively mitigates optimization challenges and significantly reduces flow field reconstruction error.

C2weakest assumption

That the causal decoupling strategy, dual-network architecture, and Taylor-based high-order loss will reliably overcome severe optimization difficulties and generalization failures of standard PINNs specifically in highly tortuous intracranial geometries under extremely sparse data constraints.

C3one line summary

DCP-INN combines a diamond-shaped main network for low-frequency flow trends with a parallel correction network for high-frequency residuals, plus a Taylor-expansion high-order loss, to reconstruct hemodynamics accurately from sparse data in tortuous vessels.

References

40 extracted · 40 resolved · 0 Pith anchors

[1] A review of 3D vessel lumen segmentation techniques: models, features and extraction schemes, 2009

[2] Blood vessel segmentation algorithms—Review of methods, datasets and evaluation metrics, 2018

[3] Fluid-structure interaction analysis of pulsatile flow in arterial aneurysms with physics-informed neural networks and computational fluid dynamics, 2025

[4] Automated generation of 0D and 1D reduced-order models of patient-specific blood flow, 2022

[5] Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations, 2018

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-18T03:10:02.274961Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

4c54914f99993d33f2bef0e4b315a33c9ac96a32dc40721a66bccec235aeeee4

Aliases

arxiv: 2605.12544 · arxiv_version: 2605.12544v1 · doi: 10.48550/arxiv.2605.12544 · pith_short_12: JRKJCT4ZTE6T · pith_short_16: JRKJCT4ZTE6TH4V6 · pith_short_8: JRKJCT4Z

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/JRKJCT4ZTE6TH4V66DSLGFNDHS \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 4c54914f99993d33f2bef0e4b315a33c9ac96a32dc40721a66bccec235aeeee4

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "ed0f3fbc1b2477e11e8061996296bc66fd5dac2f4e5e460069243be1d10ce146",
    "cross_cats_sorted": [],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "physics.med-ph",
    "submitted_at": "2026-05-09T09:32:51Z",
    "title_canon_sha256": "5cc38d388f5b60c11c0731e59c49282c869b7c02629ec3f63088fd64a7967a3c"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.12544",
    "kind": "arxiv",
    "version": 1
  }
}