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Filtering by: Creator Smith, Selena Y. Degree Field Mathematics Commencement Year Unknown

Quantifying uncertainty with stochastic collocation in the kinematic magentohydrodynamic framework

Creator:

Rajbhandari, Evan, Gibson, Nathan L., and Woodside, C. Rigel

Abstract:

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...

Resource Type:

Preprint

Full Text:

deterministic and random function spaces, or V := V (D)× L2,p(Ω) = { f : D × Ω→ R3 ∣∣∣ f(·, y) ∈ (L2,p(Ω))3, f
Stochastic Collocation Error Analysis Within a Kinematic Magnetohydrodynamics Framework

Creator:

Rajbhandari, Evan, Gibson, Nathan L., and Woodside, C. Rigel

Abstract:

We explore one numerical method for dealing with uncertainty quantification, stochastic collocation. We adapt this method for the uncertain kinematic magnetohydrodynamic system. We then demonstrate well-posedness of the uncertain forward problem. We also describe the method in detail, and perform an error analysis of the method, describing the necessary assumptions...

Resource Type:

Preprint

Full Text:

→ R3 ∣∣∣ f(·, y) ∈ (L2,p(Ω))3, f(x, ·) ∈ V }. Similarly, define W := W (D) × L2,p(Ω) as the random