Evidence map›Paper›PMID 41200021›Full record

ArticleJournal of scientific computing2025

Neural Active Manifolds: Nonlinear Dimensionality Reduction for Uncertainty Quantification.

Andrea Zanoni, Gianluca Geraci, Matteo Salvador, Alison L Marsden, Daniele E Schiavazzi

Abstract read
In one paragraph

Article in Journal of scientific computing, 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. 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

5 authors.

Andrea ZanoniCentro di Ricerca Matematica Ennio De Giorgi, Scuola Normale Superiore, Pisa, Italy.ORCID 0000-0002-7337-288X
Gianluca GeraciCenter for Computing Research, Sandia National Laboratories, Albuquerque, NM USA.
Matteo SalvadorInstitute for Computational and Mathematical Engineering, Stanford University, Stanford, CA USA.
Alison L MarsdenInstitute for Computational and Mathematical Engineering, Stanford University, Stanford, CA USA.
Daniele E SchiavazziDepartment of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, IN USA.

Funding

Uncertainty aware virtual treatment planning for peripheral pulmonary artery stenosisR01HL167516 · NHLBI · STANFORD UNIVERSITY · PI Jeffrey A. Feinstein, Alison L Marsden · 2023 to 2026
$2.7M
SCH: INT: A Virtual Surgery Simulator to Accelerate Medical Training in Cardiovascular Disease R01EB029362 · NIBIB · STANFORD UNIVERSITY · PI MARSDEN, ALISON L · 2019 to 2022
$1.4M
NHLBI NIH HHS R01 HL167516NIBIB NIH HHS R01 EB029362
6 · The paper itself

Abstract

We present a new approach for nonlinear dimensionality reduction, specifically designed for computationally expensive mathematical models. We leverage autoencoders to discover a one-dimensional

Indexed as

AutoencodersDimensionality reductionMultifidelity estimatorsSensitivity analysisSurrogate modelingUncertainty quantification

Identifiers

PMID41200021
PMCPMC12586266

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.