Effective Quadratures or "equadratures" is our 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 has benefitted from contributions from folks in the University of Cambridge, Imperial College London, Stanford University, The University of Utah, The Alan Turing Institute and the University of Cagliari.
To explore the capabilities of equadratures, without directly downloading the code, you can use either Google Colab or Microsoft Azure; click the images below to start coding! Regardless of your choice, please read the start-up instructions here.