Michael Dikovsky
Physicist by education, Michael Dikovsky is interested in diverse areas of science and engineering: Software Engineering, Mathematical Physics and Scientific Computing, Plasma Physics, Spectroscopy, Quantum Mechanics, Clean Energy, Power Grid, Electronics, FPGAs, Compilers, Formal Logic, Deterministic and Stochastic Optimization Methods, Multi-physics Simulation and Modeling of Complex Systems.
Current research focus is on understanding behavior of nuclear fusion plasma using advanced bayesian inference methods.
Authored Publications
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Comprehensive Imaging of C-2W Plasmas: Instruments and Applications
Erik Granstedt
Deepak Gupta
James Sweeney
Matthew Tobin
the TAE team
Review of Scientific Instruments, 92 (2021), pp. 043515
Modeling the effect of exposure notification and non-pharmaceutical interventions on COVID-19 transmission in Washington state
Matthew Abueg
Robert Hinch
Neo Wu
William Probert
Austin Wu
Paul Eastham
Yusef Shafi
Matt Rosencrantz
Zhao Cheng
Anel Nurtay
Lucie Abeler-Dörner
David Bonsall
Michael V. McConnell
Shawn O'Banion
Christophe Fraser
npj Digital Medicine (2021)
Multi-instrument Bayesian reconstruction of plasma shape evolution in C-2W experiment
Erik Trask
Hiroshi Gota
Ian Langmore
Jesus Romero
Rob von Behren
Tom Madams
Physics of Plasmas (2021)
OVERVIEW OF C-2W: HIGH TEMPERATURE, STEADY-STATE BEAM-DRIVEN FIELD-REVERSED CONFIGURATION PLASMAS
Ian Langmore
Rob von Behren
TAE
Tom Madams
William D Heavlin
Nuclear Fusion (2021)
Fusion Plasma Reconstruction
Ian Langmore
Nathan Neibauer
Rob von Behren
(2019)
Application of Bayesian inference for reconstruction of FRC plasma state in C-2W
Erik Trask
Hiroshi Gota
Ian Langmore
Jesus Romero
Matthew Thompson
Tom Madams
Yair Carmon
(2018)
The Plasma Debugger
Erik Granstedt
Erik Trask
Hiroshi Gota
Ian Langmore
Jesus Romero
Matthew Thompson
Roberto Mendoza
Tom Madams
Yair Carmon
(2018)
