Evidence map›Paper›PMID 31065346›Full record

ArticleInterface focus2019

Linking Gaussian process regression with data-driven manifold embeddings for nonlinear data fusion.

Seungjoon Lee, Felix Dietrich, George E Karniadakis, Ioannis G Kevrekidis

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Interface focus, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

4 authors.

Seungjoon LeeDepartment of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA.ORCID 0000-0003-4586-3574
Felix DietrichDepartment of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA.ORCID 0000-0002-2906-1769
George E KarniadakisDivision of Applied Mathematics, Brown University, Providence, RI, USA.ORCID 0000-0002-9713-7120
Ioannis G KevrekidisDepartment of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA.ORCID 0000-0003-2220-3522

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In statistical modelling with Gaussian process regression, it has been shown that combining (few) high-fidelity data with (many) low-fidelity data can enhance prediction accuracy, compared to prediction based on the few high-fidelity data only. Such information fusion techniques for multi-fidelity data commonly approach the high-fidelity model

Indexed as

machine learningmulti-fidelity datamulti-resolution simulation

Identifiers

PMID31065346
PMCPMC6501345

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.