A technique for characterising feature size and quality of manifolds-Reference-Cited by-同舟云学术

A technique for characterising feature size and quality of manifolds

Published:2021-06-03 Issue:4 Volume:25 Page:646-668
ISSN:1364-7830
Container-title:Combustion Theory and Modelling
language:en
Short-container-title:Combustion Theory and Modelling

Author:

Armstrong Elizabeth¹^ORCID,Sutherland James C.¹

Affiliation:

1. Department of Chemical Engineering, University of Utah, Salt Lake City, UT, USA

Funder

Sandia National Laboratories

Publisher

Informa UK Limited

Subject

General Physics and Astronomy,Energy Engineering and Power Technology,Fuel Technology,Modelling and Simulation,General Chemical Engineering,General Chemistry

Link

https://www.tandfonline.com/doi/pdf/10.1080/13647830.2021.1931715

Reference36 articles.

1. Nonlinear Dimensionality Reduction by Locally Linear Embedding

2. A Global Geometric Framework for Nonlinear Dimensionality Reduction

3. Principal Manifolds and Nonlinear Dimensionality Reduction via Tangent Space Alignment

4. Unsupervised Learning of Image Manifolds by Semidefinite Programming

Cited by 14 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Comparisons of Different Representative Species Selection Schemes for Reduced-Order Modeling and Chemistry Acceleration of Complex Hydrocarbon Fuels;Energies;2024-05-28

2. Data-driven models and digital twins for sustainable combustion technologies;iScience;2024-04

3. An a priori analysis on principal component analysis based conditional source-term estimation model for Sandia jet flames;Combustion and Flame;2024-02

4. A co-kurtosis PCA based dimensionality reduction with nonlinear reconstruction using neural networks;Combustion and Flame;2024-01

5. Reduced-order modeling with reconstruction-informed projections;Combustion and Flame;2024-01