Digital Engineering Conference

DICE serves as a virtual center to formalize and coordinate digital engineering, digital twinning, and digital transformation activities across next generation energy systems.
Upcoming Events
CAES Auditorium, 995 MK Simpson Blvd, Idaho Falls, ID 83401 (Support Virtual Attendees)

RAVEN Workshop

RAVEN is a multi-purpose stochastic platform that integrates uncertainty propagation, machine learning, optimization, and data analysis methods, and it provides a unique language to apply these methods to user-provided simulation models. With RAVEN, users can create customizable statistical analysis workflows where the response of simulation models is explored (e.g., for uncertainty propagation, model optimization, model calibration and model validation) for a variety of initial and operating conditions and the resulting data can be analyzed using machine learning, data mining and artificial intelligence algorithms. These analysis workflows can be executed on multiple operating systems and hardware configurations, ranging from laptops to high performance computing environments. RAVEN also provides a plug-in interface that has already been leveraged by many system analysis and design tools, which enable simple multi-code integration across simulation tools.

An overview of the software is available at https://github.com/idaholab/raven/wiki or https://raven.inl.gov

The software is open source and can be downloaded at: https://github.com/idaholab/raven

Training Objectives:

The first objective is to provide a general understanding of the RAVEN package and its main capabilities. Second, a series of practical examples will be provided in ascending level of complexity, starting from the simplest statistical analysis to the generation of the complex machine learning models and their utilization in system analysis and uncertainty quantification. Third, the system optimization, such as genetic algorithm and Bayesian optimization will be covered. At the end of the training activities, attendees will be able to autonomously use the code in one or more of the RAVEN applications’ areas. Each training section will include a theoretical/code usage overview of the subject capability and hands-on activities (construction of the RAVEN input and execution of the analysis); for this reason, it is suggested that the attendees have their own laptop ready and have RAVEN installed before the training. We recommend the attendees follow the installation procedures provided in https://pypi.org/project/raven-framework/. During the training, A RAVEN team member will be available for solving unexpected problems.