Proper orthogonal decomposition

Machine learning and data mining
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The proper orthogonal decomposition is a numerical method that enables a reduction in the complexity of computer intensive simulations such as computational fluid dynamics and structural analysis (like crash simulations). Typically in fluid dynamics and turbulences analysis, it is used to replace the Navier–Stokes equations by simpler models to solve.

Proper orthogonal decomposition is associated with model order reduction. The orthogonally decomposed model can be characterized as a surrogate model; to this end, the method is also associated with the field of machine learning.