Evidence map›Paper›PMID 39007008›Full record

ArticleNational science review2024

Enhancing neural operator learning with invariants to simultaneously learn various physical mechanisms.

Siran Li, Chong Liu, Hao Ni

Abstract read
In one paragraph

Article in National science review, 2024. 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.

Siran LiSchool of Mathematical Sciences, Shanghai Jiao Tong University, China.
Chong LiuInstitute of Mathematical Sciences, ShanghaiTech University, China.
Hao NiDepartment of Mathematics, University College London, UK.ORCID https://orcid.org/0000-0001-5485-4376

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We discuss the recent advancement in PDE learning, focusing on Physics Invariant Attention Neural Operator (PIANO). PIANO is a novel neural operator learning framework for deciphering and integrating physical knowledge from PDEs sampled from multi- physical scenarios.

Identifiers

PMID39007008
PMCPMC11242445

What OpenQuestion holds

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Registered trials

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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.