Editing Proper orthogonal decomposition (section)

{{Short description|Numerical method that reduces the complexity of computationally intensive simulations}}{{Machine learning bar}}

The '''proper orthogonal decomposition''' is a [[Numerical analysis|numerical method]] that enables a reduction in the complexity of computer intensive simulations such as [[computational fluid dynamics]] and [[structural analysis]] (like [[crash simulation]]s). Typically in [[fluid dynamics]] and [[Turbulence modeling|turbulences analysis]], it is used to replace the [[Navier–Stokes equations]] by simpler models to solve.<ref>{{Cite journal|last1=Berkooz|first1=G|last2=Holmes|first2=P|last3=Lumley|first3=J L|date=January 1993|title=The Proper Orthogonal Decomposition in the Analysis of Turbulent Flows|url=http://dx.doi.org/10.1146/annurev.fl.25.010193.002543|journal=Annual Review of Fluid Mechanics|volume=25|issue=1|pages=539–575|doi=10.1146/annurev.fl.25.010193.002543|bibcode=1993AnRFM..25..539B|issn=0066-4189}}</ref> 

It belongs to a class of algorithms called ''[[model order reduction]]'' (or in short ''model reduction''). What it essentially does is to train a model based on simulation data. To this extent, it can be associated with the field of [[machine learning]].