Official Release of the POEM code.
Version: 0.1
Website: https://idaholab.github.io/POEM/
POEM is a platform for optimal experiment management, powered with automated machine learning to accelerate the discovery of optimal solutions, and automatically guide the design of experiments to be evaluated. POEM currently supports 1) random model explorations for experiment design, 2) sparse grid model explorations with Gaussian Polynomial Chaos surrogate model to accelerate experiment design ,3) time-dependent model sensitivity and uncertainty analysis to identify the importance features for experiment design, 4) model calibrations via Bayesian inference to integrate experiments to improve model performance, and 5) Bayesian optimization for optimal experimental design. In addition, POEM aims to simplify the process of experimental design for users, enabling them to analyze the data with minimal human intervention, and improving the technological output from research activities.
POEM leverages Risk Analysis Virtual Environment (RAVEN: https://github.com/idaholab/raven), which is a robust platform to support model explorations and decision making, to enable large scalability, reduce computational costs, and provides access to complex physical models when performing optimal experimental design.