Recent Developments in Bayesian Inference of Tokamak Plasma Equilibria and High-Dimensional Stochastic Quadratures

We present recent results and technical breakthroughs for the Bayesian inference of tokamak equilibria using force-balance as a prior constraint. Issues surrounding model parameter representation and posterior analysis are discussed and addressed. These points motivate the recent advancements embodi...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2014-05
Main Authors: von Nessi, G T, Hole, M J
Format: Article
Language:English
Subjects:
Bayesian analysis
Computer simulation
Constraint modelling
Economic models
Equilibrium
Equilibrium analysis
Quadratures
Software
Statistical inference
Tokamak devices
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:We present recent results and technical breakthroughs for the Bayesian inference of tokamak equilibria using force-balance as a prior constraint. Issues surrounding model parameter representation and posterior analysis are discussed and addressed. These points motivate the recent advancements embodied in the Bayesian Equilibrium Analysis and Simulation Tool (BEAST) software being presently utilised to study equilibria on the Mega-Ampere Spherical Tokamak (MAST) experiment in the UK (von Nessi \emph{et. al.} 2012 \emph{J. Phys. A} \textbf{46} 185501). State-of-the-art results of using BEAST to study MAST equilbria are reviewed, with recent code advancements being systematically presented though out the manuscript.
ISSN:2331-8422
DOI:10.48550/arxiv.1403.1321