Evidence map›Paper›PMID 39726586›Full record

ArticleComputers & mathematics with applications (Oxford, England : 1987)2024

Companion-based multi-level finite element method for computing multiple solutions of nonlinear differential equations.

Wenrui Hao, Sun Lee, Young Ju Lee

Abstract read
In one paragraph

Article in Computers & mathematics with applications (Oxford, England : 1987), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

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3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Article
  2. ZENN: A thermodynamics-inspired computational framework for heterogeneous data-driven modeling.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  3. Article
  4. Article
  5. Newton Informed Neural Operator for Solving Nonlinear Partial Differential Equations.Advances in neural information processing systems · 2024
    Article
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.

Wenrui HaoDepartment of Mathematics, Penn State, 16802 PA, State College, USA.
Sun LeeDepartment of Mathematics, Penn State, 16802 PA, State College, USA.
Young Ju LeeDepartment of Mathematics, Texas State, 78666 TX, San Marcos, USA.

Funding

A pathophysiology driven spatial dynamic modeling framework for personalized prediction and precision medicineR35GM146894 · NIGMS · PENNSYLVANIA STATE UNIVERSITY, THE · PI WENRUI HAO · 2022 to 2026
$2.0M
NIGMS NIH HHS R35 GM146894
6 · The paper itself

Abstract

The utilization of nonlinear differential equations has resulted in remarkable progress across various scientific domains, including physics, biology, ecology, and quantum mechanics. Nonetheless, obtaining multiple solutions for nonlinear differential equations can pose considerable challenges, particularly when it is difficult to find suitable initial guesses. To address this issue, we propose a pioneering approach known as the Companion-Based Multilevel Finite Element Method (CBMFEM). This novel technique efficiently and accurately generates multiple initial guesses for solving nonlinear elliptic semi-linear equations containing polynomial nonlinear terms through the use of finite element methods with conforming elements. As a theoretical foundation of CBMFEM, we present an appropriate and new concept of the isolated solution to the nonlinear elliptic equations with multiple solutions. The newly introduced concept is used to establish the inf-sup condition for the linearized equation around the isolated solution. Furthermore, it is crucially used to derive a theoretical error analysis of finite element methods for nonlinear elliptic equations with multiple solutions. A number of numerical results obtained using CBMFEM are then presented and compared with a traditional method. These not only show the CBMFEM's superiority, but also support our theoretical analysis. Additionally, these results showcase the effectiveness and potential of our proposed method in tackling the challenges associated with multiple solutions in nonlinear differential equations with different types of boundary conditions.

Indexed as

Boundary conditionsElliptic semilinear PDEsFinite element methodMultiple solutionsNonlinear ODEs

Identifiers

PMID39726586
PMCPMC11671158

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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.