Model order reduction

The traditional means of emulating a complicated model is to find an approximation to it in the sense of using some kind of dominant subspace to summarise the behaviour of the model.

A highly-developed, powerful field with dense jargon.


Amsallem, David, Matthew J. Zahr, and Charbel Farhat. 2012. Nonlinear Model Order Reduction Based on Local Reduced-Order Bases.” International Journal for Numerical Methods in Engineering 92 (10): 891–916.
Ghattas, Omar, and Karen Willcox. 2021. Learning Physics-Based Models from Data: Perspectives from Inverse Problems and Model Reduction.” Acta Numerica 30 (May): 445–554.
Gladish, Daniel W., Daniel E. Pagendam, Luk J. M. Peeters, Petra M. Kuhnert, and Jai Vaze. 2018. Emulation Engines: Choice and Quantification of Uncertainty for Complex Hydrological Models.” Journal of Agricultural, Biological and Environmental Statistics 23 (1): 39–62.
Peherstorfer, Benjamin, and Karen Willcox. 2015. Dynamic Data-Driven Reduced-Order Models.” Computer Methods in Applied Mechanics and Engineering 291 (July): 21–41.
Pestourie, Raphaël, Youssef Mroueh, Chris Rackauckas, Payel Das, and Steven G. Johnson. 2022. Physics-Enhanced Deep Surrogates for PDEs.” arXiv.
Siade, Adam J., Tao Cui, Robert N. Karelse, and Clive Hampton. 2020. Reduced‐Dimensional Gaussian Process Machine Learning for Groundwater Allocation Planning Using Swarm Theory.” Water Resources Research 56 (3).
Zahr, Matthew J., and Charbel Farhat. 2015. Progressive Construction of a Parametric Reduced-Order Model for PDE-Constrained Optimization.” International Journal for Numerical Methods in Engineering 102 (5): 1111–35.

No comments yet. Why not leave one?

GitHub-flavored Markdown & a sane subset of HTML is supported.