Evidence map›Paper›PMID 42818495›Full record

ArticleResearch square2026

PatternFormer: Learning Multiple Solution Patterns in Reaction-Diffusion Systems.

Zhipeng Chang, Wenpeng Yin, Wenrui Hao

Abstract readPreprint
In one paragraph

Article in Research square, 2026. 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

3 authors.

Zhipeng ChangDepartment of Mathematics, Penn State University, University Park, PA, USA.
Wenpeng YinDepartment of Computer Science and Engineering, Penn State University, University Park, PA, USA.
Wenrui HaoDepartment of Mathematics, Penn State University, University Park, PA, 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

Many nonlinear models across physics, chemistry, and biology exhibit multiple solutions for the same parameters, and capturing this entire solution set is essential for understanding pattern-forming systems. Yet existing learned surrogates are fundamentally single-valued: neural operators map each parameter to a single output, and physics-informed neural networks converge to one branch. We develop

Identifiers

PMID42818495
PMCPMC13622797

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.