Bereiche | Tage | Auswahl | Suche | Aktualisierungen | Downloads | Hilfe
P: Fachverband Plasmaphysik
P 25: Poster III
P 25.2: Poster
Donnerstag, 29. Februar 2024, 16:30–18:30, ELP 6: Foyer
Uncertainty Quantification for Magnetohydrodynamic Equilibrium Reconstruction: A data driven approach — •Robert Köberl1,2, Robert Babin3, and Christopher G. Albert3 — 1MPI for Plasma Physics, Garching, Germany — 2CIT, TU Munich, Garch- ing, Germany — 3Fusion@ÖAW, ITPcp, TU Graz, Graz, Austria
We report on progress towards a probabilistic framework for uncertainty quantification and propagation in analysis and numerical modeling of physics in magnetically confined plasmas in the stellarator configuration. A frequent starting point in this process is the calculation of a magnetohydrodynamic equilibrium from plasma profiles. Profiles and therefore the equilibrium are typically reconstructed from experimental data. What sets equilibrium reconstruction apart from usual inverse problems is that profiles are given as functions over a magnetic flux derived from the magnetic field, rather than spatial coordinates. This makes it a fixed-point problem that is traditionally left inconsistent or solved iteratively in a least-squares sense. The aim here is towards a straightforward and transparent process to quantify and propagate uncertainties and their correlations for function-valued fields and profiles in this setting. We propose a Bayesian inference framework that utilizes a low dimensional prior distribution of equilibria, constructed with principal component analysis. Additionally, neural-network- and polynomial-regression-surrogates of the forward model for synthetic diagnostics are trained. This enables faster sam- pling when approximating the posterior distribution of equilibria via Markov chain Monte Carlo sampling.
Keywords: dimensionality reduction; Bayesian analysis; uncertainty quantification; MHD equilibrium reconstruction; surrogate model