Open AccessQuantifying uncertainty with stochastic collocation in the kinematic magentohydrodynamic framework
Author(s) -
Evan Rajbhandari,
Nathan L. Gibson,
Rigel Woodside
Publication year - 2022
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2207/1/012007
Subject(s) - randomness , kinematics , collocation (remote sensing) , uncertainty quantification , magnetohydrodynamic drive , variance (accounting) , mathematics , collocation method , computer science , mathematical optimization , mathematical analysis , statistics , magnetohydrodynamics , physics , classical mechanics , ordinary differential equation , plasma , accounting , quantum mechanics , machine learning , business , differential equation
We discuss an efficient numerical method for the uncertain kinematic magnetohydrodynamic system. We include aleatoric uncertainty in the parameters, and then describe a stochastic collocation method to handle this randomness. Numerical demonstrations of this method are discussed. We find that the shape of the parameter distributions affect not only the mean and variance, but also the shape of the solution distributions.