PCAfold 2.0—Novel tools and algorithms for low-dimensional manifold assessment and optimization-Reference-Cited by-同舟云学术

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PCAfold 2.0—Novel tools and algorithms for low-dimensional manifold assessment and optimization

Published:2023-07 Issue: Volume:23 Page:101447
ISSN:2352-7110
Container-title:SoftwareX
language:en
Short-container-title:SoftwareX

Author:

Zdybał Kamila^ORCID,Armstrong Elizabeth,Parente Alessandro,Sutherland James C.

Funder

National Nuclear Security Administration

Sandia National Laboratories

National Science Foundation

Horizon 2020 Framework Programme

European Research Council

Fonds De La Recherche Scientifique - FNRS

U.S. Department of Energy

Publisher

Elsevier BV

Subject

Computer Science Applications,Software

Reference12 articles.

1. PCAfold: Python software to generate, analyze and improve PCA-derived low-dimensional manifolds;Zdybał;SoftwareX,2020

2. A technique for characterising feature size and quality of manifolds;Armstrong;Combust Theory Model,2021

3. Cost function for low-dimensional manifold topology assessment;Zdybał;Sci Rep,2022

4. Partition of unity networks: Deep hp-approximation;Lee,2021

5. Accurate compression of tabulated chemistry models with partition of unity networks;Armstrong;Combust Sci Technol,2022

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