EasyVVUQ: Uncertainty intervals for everyone!¶
EasyVVUQ is a Python library designed to facilitate verification, validation and uncertainty quantification (VVUQ) for a wide variety of simulations. It was conceived and developed within the EU funded VECMA (Verified Exascale Computing for Multiscale Applications) project.
Goals¶
The purpose of EasyVVUQ is to make it as easy as possible to implement advanced techniques for uncertainty quantification for existing application codes (or workflows). We do not intend to re-invent the wheel, and plan on always building upon existing libraries such as Chaospy which focus on providing statistical functionality. Our aim is to expose these features in as accessible a way for users of scientific codes, in particular simulation software targeting HPC machines.
For technical details please see EasyVVUQ API Reference.
Table of contents¶
- EasyVVUQ installation
- Conceptual basis
- EasyVVUQ tutorials
- EasyVVUQ - Basic Concepts
- EasyVVUQ execution on HPC machines with QCG-PilotJob
- EasyVVUQ and Cloud Execution via Kubernetes
- EasyVVUQ fusion tutorial
- Run the fusion EasyVVUQ campaign
- Dimension adaptive sampling tutorial
- MCMC in EasyVVUQ
- Markov-Chain Monte Carlo in EasyVVUQ
- EasyVVUQ - Vector Quantities of Interest
- EasyVVUQ Mega Tutorial
- Basic Tutorial
- A Cooling Coffee Cup with Polynomial Chaos Expansion
- A Cooling Coffee Cup - Using Dask Jobqueue to Run on Clusters
- A Reduced Version of the Fusion Workflow using Polynomial Chaos Expansion
- Combining multiple samplers using Multisampler
- Restarting a Campaign
- Writing a custom encoder or decoder
- Creating complex encoders using MultiEncoder and DirectoryBuilder
- Hierarchical sparse grid tutorial
- Validation by comparing QoI distributions