Late February the paper "Bayesian optimization of massive material injection for disruption mitigation in tokamaks" was accepted for publication in the Journal of Plasma Physics. The preprint is available on arXiv.
The paper reports results from early work in the project "OptiFun: Optimising Fusion with Functional Programming" where our group at the Chalmers Functional Programming Unit is working together with the Plasma Theory group.
The paper describes the "landscape" of how two control parameters (injected amounts of neon and deuterium) affect four objectives related to the physical processes of a disruption event in a tokamak fusion experiment (when simulated with DREAM = Disruption and Runaway Electron Analysis Model).
The paper reports results from early work in the project "OptiFun: Optimising Fusion with Functional Programming" where our group at the Chalmers Functional Programming Unit is working together with the Plasma Theory group.
The paper describes the "landscape" of how two control parameters (injected amounts of neon and deuterium) affect four objectives related to the physical processes of a disruption event in a tokamak fusion experiment (when simulated with DREAM = Disruption and Runaway Electron Analysis Model).
