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Polynomial approximations for computer models and data analytics.

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Uncertainty quantification

Polynomial chaos with independent and correlated input parameters using tensor and sparse grids, least squares, or compressive sensing techniques.

Dimension reduction

Data-driven dimension reduction using active subspaces and polynomial ridge approximations; Sobol' indices for understanding parameter importance.

Machine learning

Piecewise polynomial regression using trees along with functions for estimating posterior variance. Optimisation using trust-region based polynomial surrogates.

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Effective Quadratures is a collection of utilities for understanding the input-output nature of models and data. Our tools are powered by our open-source code equadratures. The latest version of the code is Narwhal v9.0. To download and install the code, please use the python package index command:

pip install equadratures

To set the code up, try the following:

from equadratures import *
import numpy as np

def my_model(x):
	return x**2 + 0.2 * x - np.sin(x)

parameter = Parameter(distribution='uniform', lower=-1., upper=1., order=3)
basis = Basis('univariate')
poly = Poly(parameter, basis, 'numerical-integration')
poly.set_model(my_model)