171 research outputs found
Tutorial applications for Verification, Validation and Uncertainty Quantification using VECMA toolkit
The VECMA toolkit enables automated Verification, Validation and Uncertainty Quantification (VVUQ) for complex applications that can be deployed on emerging exascale platforms and provides support for software applications for any domain of interest. The toolkit has four main components including EasyVVUQ for VVUQ workflows, FabSim3 for automation and tool integration, MUSCLE3 for coupling multiscale models and QCG tools to execute application workflows on high performance computing (HPC). A more recent addition to the VECMAtk is EasySurrogate for various types of surrogate methods. In this paper, we present five tutorials from different application domains that apply these VECMAtk components to perform uncertainty quantification analysis, use surrogate models, couple multiscale models and execute sensitivity analysis on HPC. This paper aims to provide hands-on experience for practitioners aiming to test and contrast with their own applications
Epistemic and Aleatoric Uncertainty Quantification and Surrogate Modelling in High-Performance Multiscale Plasma Physics Simulations
This work suggests several methods of uncertainty treatment in multiscale
modelling and describes their application to a system of coupled turbulent
transport simulations of a tokamak plasma. We propose a method to quantify the
usually aleatoric uncertainty of a system in a quasi-stationary state,
estimating the mean values and their errors for quantities of interest, which
is average heat fluxes in the case of turbulence simulations. The method
defines the stationarity of the system and suggests a way to balance the
computational cost of simulation and the accuracy of estimation. This allows,
contrary to many approaches, to incorporate aleatoric uncertainties in the
analysis of the model and to have a quantifiable decision for simulation
runtime. Furthermore, the paper describes methods for quantifying the epistemic
uncertainty of a model and the results of such a procedure for turbulence
simulations, identifying the model's sensitivity to particular input parameters
and sensitivity to uncertainties in total. Finally, we introduce a surrogate
model approach based on Gaussian Process Regression and present a preliminary
result of training and analysing the performance of such a model based on
turbulence simulation data. Such an approach shows a potential to significantly
decrease the computational cost of the uncertainty propagation for the given
model, making it feasible on current HPC systems
Uncertainty quantification patterns for multiscale models
Uncertainty quantification (UQ) is a key component when using computational models that involve uncertainties, e.g. in decision-making scenarios. In this work, we present uncertainty quantification patterns (UQPs) that are designed to support the analysis of uncertainty in coupled multi-scale and multi-domain applications. UQPs provide the basic building blocks to create tailored UQ for multiscale models. The UQPs are implemented as generic templates, which can then be customized and aggregated to create a dedicated UQ procedure for multiscale applications. We present the implementation of the UQPs with multiscale co
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