Package: equadratures
# equadraturesequadraturesis an open-source library foruncertainty quantification,machine learning,optimisation,numerical integrationanddimension reduction– all using orthogonal polynomials. It is particularly useful for models / problems where output quantities of interest are smooth and continuous; to this extent it has found widespread applications in computational engineering models (finite elements, computational fluid dynamics, etc). It is built on the latest research within these areas and has both deterministic and randomised algorithms.Key words associated with this code: polynomial surrogates, polynomial chaos, polynomial variable projection, Gaussian quadrature, Clenshaw Curtis, polynomial least squares, compressed sensing, gradient-enhanced surrogates, supervised learning.## CodeThe latest version of the code is v10Baby Blue, released March 2022.![](https://travis-ci.com/equadratures/equadratures.svg?branch=master)
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[![](https://img.shields.io/discourse/status?server=https%3A%2F%2Fdiscourse.equadratures.org)](https://discourse.equadratures.org)If you usepipyou can install the code with:`python pip install equadratures `orpipcan be replaced withpython -m pip, wherepythonis the python version you wish to installequadraturesfor. Use of a virtual enviroment such as [virtualenv](https://pypi.org/project/virtualenv/) or [pyenv](https://github.com/pyenv/pyenv)/[pipenv](https://pypi.org/project/pipenv/) is also encouraged. Alternatively you can click either on theFork Codebutton orClone, and install from your local version of the code.For issues with the code, please doraise an issueon our Github page; do make sure to add the relevant bits of code and specifics on package version numbers. We welcome contributions and suggestions from both users and folks interested in developing the code further.Our code is designed to require minimal dependencies; current package requirements includenumpy,scipyandmatplotlib.If, and onlyifusing theGraphPolysclass, additional requirements would include thenetworkx, andtorchmodules.## Documentation, tutorials, DiscourseCode documentation and details on the syntax can be found [here](https://equadratures.org/index.html).We’ve recently started a Discourse forum! Check it out [here](https://discourse.equadratures.org/).## Code objectivesSpecific goals of this code include:probability distributions and orthogonal polynomialssupervised machine learning: regression and compressive sensingnumerical quadrature and high-dimensional samplingtransforms for correlated parameterscomputing moments from models and data-setssensitivity analysis and Sobol’ indicesdata-driven dimension reductionridge approximationssurrogate-based design optimisation## Get in touchFeel free to follow us via [Twitter](https://twitter.com/EQuadratures) or email us atmail@equadratures.org.## Community guidelinesIf you have contributions, questions, or feedback use either the Github repository, or get in touch. We welcome contributions to our code. In this respect, we follow the [NumFOCUS code of conduct](https://numfocus.org/code-of-conduct).## AcknowledgmentsThis work was supported by wave 1 of The UKRI Strategic Priorities Fund under the EPSRC grant EP/T001569/1, particularly the [Digital Twins in Aeronautics](https://www.turing.ac.uk/research/research-projects/digital-twins-aeronautics) theme within that grant, and [The Alan Turing Institute](https://www.turing.ac.uk).