DiFVM: A Vectorized Graph-Based Finite Volume Solver for Differentiable CFD on Unstructured Meshes.

Pan Du, Yongqi Li, Mingqi Xu, Jian-Xun Wang

Abstract read

In one paragraph

Article in Journal of computational physics, 2027. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Pan DuDepartment of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, IN.

Yongqi LiDepartment of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, IN.

Mingqi XuSibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, NY, USA.

Jian-Xun WangDepartment of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, IN.

Funding

SCH: Efficient Image-based Hemodynamic Modeling via Physics-integrated Bayesian Deep LearningR01HL177814 · NHLBI · UNIVERSITY OF NOTRE DAME · PI Jian-Xun Wang · 2024 to 2026

$888k

NHLBI NIH HHS R01 HL177814

6 · The paper itself

Abstract

Differentiable programming has emerged as an important tool for gradient-based inverse problems and end-to-end hybrid physics-machine learning in computational fluid dynamics. Existing differentiable CFD platforms predominantly rely on structured Cartesian grids, limiting their direct application to geometrically complex domains where body-conforming unstructured discretizations are essential. We present

Indexed as

Cardiovascular hemodynamicsGPU-accelerationGraph message passingInverse problemsJAXPhysics-integrated machine learning

Identifiers

PMID42877889

DOI10.1016/j.jcp.2026.115429

PMCPMC13668260

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.