Skip to main navigation
Skip to search
Skip to main content
Princeton University Home
Help & FAQ
Home
Profiles
Research units
Facilities
Projects
Research output
Press/Media
Search by expertise, name or affiliation
Manifold learning for parameter reduction
Alexander Holiday
, Mahdi Kooshkbaghi
, Juan M. Bello-Rivas
, C. William Gear
, Antonios Zagaris
, Ioannis G. Kevrekidis
Chemical & Biological Engineering
Research output
:
Contribution to journal
›
Article
›
peer-review
34
Scopus citations
Overview
Fingerprint
Fingerprint
Dive into the research topics of 'Manifold learning for parameter reduction'. Together they form a unique fingerprint.
Sort by
Weight
Alphabetically
Engineering
State Variable
100%
Dimensionality
66%
Symbolics
66%
Parameter Space
33%
Model Reduction
33%
Model Parameter
33%
Learning Technique
33%
Input Space
33%
Input Parameter
33%
Complex Model
33%
Mathematics
Manifold
100%
State Variable
100%
Dynamical System
33%
Differential Equation
33%
Parameter Space
33%
Curse of Dimensionality
33%
Input Parameter
33%
Complex Model
33%
Keyphrases
Model Dimensionality
33%
Symbolic Action
33%
High-dimensional Parameter Space
33%
Computational Reduction
33%
Large-scale Dynamical Systems
33%
Computer Science
Dimensional Parameter Space
33%