Reduced Order Methods for Modeling and Computational Reduction
By Alfio Maria Quarteroni, Gianluigi Rozza. 2013
416 citations recorded by the source corpus.
Subfields: Computational Theory and Mathematics, Electrical and Electronic Engineering, Numerical Analysis
View Paper: https://www.semanticscholar.org/paper/714f42335f446a41c51dd0e923c836d41ad4bbb5
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computational Theory and Mathematics
- Benjamin Peherstorfer
- Karen E. Willcox
- Bernd R. Noack
- Michael S. Steinbach
- Vipin Kumar
- L. Mainini
- Steven L. Brunton
- Xiaowei Jia
In Electrical and Electronic Engineering
- Karen E. Willcox
- Michael S. Steinbach
- Vipin Kumar
- Benjamin Peherstorfer
- L. Mainini
- Steven L. Brunton
- Xiaowei Jia
- Shaoming Xu
In Numerical Analysis
- Steven L. Brunton
- Benjamin Peherstorfer
- L. Mainini
- Renee C. Swischuk
- Karen E. Willcox
- Thomas Duriez
- Bernd R. Noack
- Michael S. Steinbach
Citations in the corpus
- Machine Learning Control – Taming Nonlinear Dynamics and Turbulence (2016, background): “This results in a reduced-order model (ROM) [220, 24].”
- Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems (2020, methodology): “A common way to do this is to project the governing equations of a system onto a linear subspace of the original state space using a method such as principal components analysis or dynamic mode decomposition [221].”
- Projection-based model reduction: Formulations for physics-based machine learning (2019, methodology): “Numer Methods Partial Differ Equ 2013;29(5):1587–628 .”
- Integrating Physics-Based Modeling with Machine Learning: A Survey (2020, methodology): “A common way to do this is to project the governing equations of a system onto a linear subspace of the original state space using a method such as principal components analysis or dynamic mode decomposition [201].”