Evidence map›Paper›PMID 41798709›Full record

ArticleBeijing journal of pure & applied mathematics2025

Topology-preserving Hodge decomposition in the Eulerian representation.

Zhe Su, Yiying Tong, Guo-Wei Wei

Abstract read
In one paragraph

Article in Beijing journal of pure & applied mathematics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
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

3 authors.

Zhe SuDepartment of Mathematics, Michigan State University, East Lansing, MI 48824, USA.
Yiying TongDepartment of Computer Science and Engineering, Michigan State University, East Lansing, MI 48824, USA.
Guo-Wei WeiDepartment of Mathematics, Michigan State University, East Lansing, MI 48824, USA.

Funding

AI-based platform for predicting emerging vaccine-escape variants and designing mutation-proof antibodiesR01AI164266 · NIAID · UNIVERSITY OF GEORGIA · PI Guowei Wei, YONG-HUI ZHENG · 2022 to 2026
$2.7M
Discovery-Driven Mathematics and Artificial Intelligence for Biosciences and Drug DiscoveryR35GM148196 · NIGMS · UNIVERSITY OF GEORGIA · PI Guowei Wei · 2023 to 2026
$1.5M
Synthetic glycan conjugates with bacteriophage Qbeta for broad spectrum anti-salmonella vaccinesR01AI146210 · NIAID · MICHIGAN STATE UNIVERSITY · PI HUANG, XUEFEI · 2019 to 2023
$1.5M
Synergistic integration of topology and machine learning for the predictions of protein-ligand binding affinities and mutation impactsR01GM126189 · NIGMS · MICHIGAN STATE UNIVERSITY · PI WEI, GUOWEI · 2018 to 2021
$1.4M
NIAID NIH HHS R01 AI146210NIAID NIH HHS R01 AI164266NIGMS NIH HHS R01 GM126189NIGMS NIH HHS R35 GM148196
6 · The paper itself

Abstract

The Hodge decomposition is a fundamental result in differential geometry and algebraic topology, particularly in the study of differential forms on a Riemannian manifold. It has significant applications in data science. Despite extensive research in the past few decades, topology-preserving Hodge decomposition of scalar and vector fields on manifolds with boundaries in the Eulerian representation remains a challenge due to the implicit incorporation of appropriate topology-preserving boundary conditions. In this work, we present a comprehensive 5-component topology-preserving Hodge decomposition that unifies normal and tangential components in the Cartesian representation. Implicit representations of planar and volumetric regions defined by level-set functions have been developed. Numerical experiments on various objects, including single-cell RNA velocity, validate the effectiveness of our approach, confirming the expected rigorous

Indexed as

35J0558A1468U05Cartesian gridsmanifold topological analysismanifold topological learningtopological LaplacianVector field decomposition

Identifiers

PMID41798709
PMCPMC12967212

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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.