PLoM is an open source python package that implements the algorithm of Probabilistic Learning on Manifolds with and without constraints (Soize and Ghanem, 2016; Soize and Ghanem, 2019) for generating realizations of a random vector in a finite Euclidean space that are statistically consistent with a given dataset of that vector. The package mainly consists of python modules and invokes a dynamic library for more efficiently computing the gradient of the potential, and can be imported and run on Linux, macOS, and Windows platform. This repository also archives the unit/integration tests and examples of applying the algorithm to practical engineering problems.
