This page has everything you need to get started with equadratures.
To download and install the code please use the python package index command:
pip3 install equadratures
or pip3 can be replaced with python -m pip, where python is the python version you wish to install equadratures for. Use of a virtual enviroment such as virtualenv or pyenv is also encouraged. Alternatively you can click either on the Fork Code button or Clone on Github, and install from your local version of the code.
python -m pip
For issues with the code, please do raise an issue on 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 include numpy, scipy and matplotlib.
Code documentation for equadratures can be found by clicking on the links below.
The tutorials below are organised by complexity, so if you are new to the code, consider starting from the first tutorial.
Note: We are currently updating the tutorials. We apologise in advance for any inconvenience this may cause.
Foundations I: Parameter
Foundations II: Orthogonal polynomials
Foundations III: Solving linear systems for model fitting
Persistence of equadratures objects
Uncertainty quantification of CFD simulations
Sensitivity analysis for a piston model
A primer on data-driven dimension reduction
Data-driven dimension reduction in turbomachinery
Adjoint/gradient-enhanced surrogate modelling
Surrogate-based design optimisation on Rosenbrock’s function
Uncertainty quantification with correlations for a borehole model
Time-series remaining useful life prediction with spatio-temporal polynomials
Flow-field estimation using vector-valued polynomial ridge approximations
Multi-fidelity Bayesian polynomials on turbine cascade experiments