AN OPEN SOURCE CODE FOR ACADEMIA, INDUSTRY AND ALL BETWEEN.
Effective Quadratures is an open-source library for uncertainty quantification, machine learning, optimisation, numerical integration and dimension 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 randomized algorithms. Effective Quadratures is actively being developed by researchers at the University of Cambridge , Stanford University, The University of Utah, The Alan Turing Institute and the University of Cagliari.
DOWNLOAD & INSTALL
pip install equadratures
pip3 install equadratures
conda install equadratures -c conda-forge
Our goal in developing Effective Quadratures has always been underscored by an ethos of inclusivity and respect. We understand the responsibility that comes with open-source code and its power; we want to help spread the capability of our tools, their underlying research, and make them available for all. When first developed, this code was tailored to replicate results found in leading papers within the field of uncertainty quantification using polynomial approximations. The community at the time did not have a single code that could be used to benchmark new sampling strategies and coefficient computation methodologies. Its utility has since grown well-beyond uncertainty quantification and now can be used in optimisation, dimension reduction and machine learning. Moving forward our hope is that this code will continue to be useful to students, researchers and industry. In line with our values, we follow the NumFOCUS code of conduct.
We routinely organize workshops covering the theory, algorithms and applications of Effective Quadratures. Some of the salient topics include:
probability distributions and orthogonal polynomials
supervised machine learning: regression and compressive sensing
numerical quadrature and high-dimensional sampling
transforms for correlated parameters
computing moments from models and data-sets
sensitivity analysis and Sobol’ indices
data-driven dimension reduction
ridge approximations and neural networks
surrogate-based design optimisation
Past workshops include:
23rd August 2019, Rolls-Royce, Derbyshire.
16th September 2019, United Kingdom Atomic Energy Authority, Oxfordshire.
If this is of interest, please do sign up below: