Application tutorials and documentations in M30 release
We have updated detailed documentations and tutorials in this M30 release which updates and merges with the previous releases. For each application tutorial, based on users feedback we obtained for this release and previous ones, we have made a huge improvement on both learning side and development side of the documentation. We divided VECMAtk tutorials into two formats, namely static and interactive. The static tutorials are accompanied with the M24 VECMAtk release while the interactive tutorials continued to be available in this release, also publicly available from the deliverable D3.5 published on the main VECMA website.
The comprehensive all-in-one VECMAtk tutorials containing all following components which establishes a platform for verification, validation and uncertainty quantification (VVUQ).
The interactive tutorials with Jupyter Notebook provide a portable training environment without requiring the installation of the VECMAtk components. Specifically, these tutorials focus on trying out FabSim3, EasyVVUQ, QCG-PilotJob and EasyVVUQ-QCGPJ using example applications. These interactive tutorials offer unique opportunities to teach and learn independently on how to perform VVUQ analysis.
The links to the detailed tutorials for the VECMAtk components and applications are provided below:
FabSim3 is an automation toolkit written in Python 3 featuring an integrated test infrastructure and a flexible plugin system. There are several plugins available from a diverse range of scientific domains, such as
- FabUQCampaign for a climate modelling;
- FabMD for a molecular dynamics modelling using Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS);
- FabFlee for a migration modelling;
- FabMogp for an earthquake modelling;
- FabCovid19 for a Covid-19 modelling.
EasyVVUQ is a Python library designed to facilitate verification, validation and uncertainty quantification (VVUQ) for a wide variety of simulations. It accounts for uncertainty quantification (UQ) and validation patterns in application to earlier described domains.
- UrbanAir for modelling air quality at street level in the cities.
UQ Sampling techniques and tutorials
- Stochastic Collocation (SC) sampler examples:
- Polynomial Chaos Expansion (PCE) sampler example:
- Fusion workflow application
- Random Sampler example:
- Ensemble of LAMMPS simulations using FabMD plugin
- Latin Hypercube technique example:
- Earthquake model using FabMogp plugin
Validation pattern tutorials:
- Ensemble Output validation in application to the FabFlee plugin
- Quantity of interest distribution extraction in application to Fusion application
QCG-Pilot Job is a Pilot Job system that allows to execute many subordinate jobs in a single scheduling system allocation.
QCG-Client is a command line client for execution of computing jobs on the clusters offered by QCG middleware.
QCG-Now is a desktop, GUI client for easy execution of computing jobs on the clusters offered by QCG middleware.
MUSCLE3 is the third incarnation of the Multiscale Coupling Library and Environment.
Application tutorials provided in M12 release
We provide four application tutorials to show that VECMAtk can be applied to solve some real world problems. These are:
- FabFlee (migration modelling)
- FabUQCampaign (basic CFD models)
- FabMD (molecular dynamics)
Each tutorial highlights different components in VECMAtk, as indicated by the corresponding figures.
In this tutorial we will explain how you can combine a simple stochastic conflict evolution model (Flare) with an agent-based migration model (Flee), perform a set of runs based on different conflict evolutions, and visualize the migrant arrivals with confidence intervals.
This tutorial describes how to create a Stochastic Collocation EasyVVUQ campaign.
This tutorial allows users to submit an entire integrated EasyVVUQ and QCG Pilot Job Manager workflow as a single job into the HPC cluster.
This example shows how to create an ensemble of LAMMPS simulations using EasyVVUQ, execute the jobs through FabMD, then analyse them within the EasyVVUQ architecture.