Uncertainty Quantification for Multiscale Fusion Plasma Simulations with VECMA Toolkit

Within VECMAtk platform we perform Uncertainty Quantification (UQ) for multiscale fusion plasmas simulations. The goal of VECMAtk is to enable modular and automated tools for a wide range of applications to archive robust and actionable results. Our aim in the current paper is to incorporate suitable features to build UQ workflow over the existing fusion codes and to tackle simulations on high performance parallel computers.

[1]  Anna Nikishova,et al.  Uncertainty Quantification of a Multiscale Model for In-Stent Restenosis , 2018, Cardiovascular engineering and technology.

[2]  J. Hammersley.  MONTE CARLO METHODS FOR SOLVING MULTIVARIABLE PROBLEMS , 1960 .

[3]  Vinzenz Gregor Eck,et al.  A guide to uncertainty quantification and sensitivity analysis for cardiovascular applications , 2016, International journal for numerical methods in biomedical engineering.

[4]  Bruno Sudret,et al.  Global sensitivity analysis using polynomial chaos expansions , 2008, Reliab. Eng. Syst. Saf..

[5]  Jalal Lakhlili,et al.  EasyVVUQ: A Library for Verification, Validation and Uncertainty Quantification in High Performance Computing , 2020 .

[6]  R. Preuss,et al.  Uncertainty quantification in ion-solid interaction simulations , 2017 .

[7]  T. Sullivan.  Introduction to Uncertainty Quantification , 2015 .

[8]  A. Saltelli,et al.  Making best use of model evaluations to compute sensitivity indices , 2002 .

[9]  Dongbin Xiu,et al.  The Wiener-Askey Polynomial Chaos for Stochastic Differential Equations , 2002, SIAM J. Sci. Comput..

[10]  A G Hoekstra,et al.  Semi-intrusive multiscale metamodelling uncertainty quantification with application to a model of in-stent restenosis , 2019, Philosophical Transactions of the Royal Society A.

[11]  Will Usher,et al.  SALib: An open-source Python library for Sensitivity Analysis , 2017, J. Open Source Softw..

[12]  Saltelli Andrea,et al.  Global Sensitivity Analysis: The Primer , 2008 .

[13]  I. Sobol,et al.  On quasi-Monte Carlo integrations , 1998 .

[14]  Krzysztof Kurowski,et al.  Development of Science Gateways Using QCG — Lessons Learned from the Deployment on Large Scale Distributed and HPC Infrastructures , 2016, Journal of Grid Computing.

[15]  Bruce D. Scott,et al.  Free-energy conservation in local gyrofluid models , 2005 .

[16]  Jet Efda Contributors,et al.  The European Integrated Tokamak Modelling (ITM) effort: achievements and first physics results , 2014 .

[17]  F. Imbeaux,et al.  The European Transport Solver , 2010, IEEE Transactions on Plasma Science.

[18]  Hamid Arabnejad,et al.  Introducing VECMAtk - Verification, Validation and Uncertainty Quantification for Multiscale and HPC Simulations , 2019, ICCS.

[19]  C. Lemieux.  Monte Carlo and Quasi-Monte Carlo Sampling , 2009 .

[20]  Kate Smith-Miles.  Exploratory data analysis , 2011 .

[21]  Olivier Hoenen,et al.  ComPat framework for multiscale simulations applied to fusion plasmas , 2019, Comput. Phys. Commun..

[22]  D. Xiu.  Numerical Methods for Stochastic Computations: A Spectral Method Approach , 2010 .

[23]  Joseph A. C. Delaney.  Sensitivity analysis , 2018, The African Continental Free Trade Area: Economic and Distributional Effects.

[24]  Hans Petter Langtangen,et al.  Chaospy: An open source tool for designing methods of uncertainty quantification , 2015, J. Comput. Sci..