George Livadiotis, Ph.D.

Research Scholar
Office Phone
171 Broadmead, 103C


George Livadiotis is a research scientist with expertise in non-equilibrium plasmas, focusing on their theoretical development within the framework of statistical mechanics, thermodynamics, and kinetic theory, with applications in space and astrophysical plasmas. His 2007 Ph.D. in Physics thesis was on nonlinear dynamics and statistical mechanics applied in solar plasma; he developed a new nonlinear dynamical model for describing the sunspot evolution, while he also generalized the statistical framework of optimization methods. His current work continues in the field of kappa distributions, the framework of statistical mechanics that describes complex systems out of the classical thermal equilibrium, varying from particle populations in space plasmas throughout the heliosphere and beyond, to multi-species population dynamics and chemical kinetics. He has published the first book on the theory of kappa distributions with applications in space plasmas; he has also performed numerous data analyses on space plasma observations to derive the thermal properties of the involved particle populations and understand the underpinning thermodynamics. Relevant analyses of data taken from the IBEX mission determined and improved understanding of the physical properties of the plasma in the outer heliosphere and the inner heliosheath, as well as their interaction with the ambient magnetic field.

Research Interests:

•    Statistical Physics & Thermodynamics: Foundation of the generalized statistical physics, thermodynamics, and kinetic theory for particle systems residing outside the classical thermal equilibrium; Theory of kappa distributions; Turbulence; Relativistic plasmas (isotropic/anisotropic distributions, temperature, and thermal variables, entropy, etc.). Applications in space & astrophysical plasmas.
•    Space Physics: 1) Model particle populations (solar wind, heliosheath, magnetospheres, pickup ions, flares); 2) Theoretical methods in plasma physics; 3) Data analysis (ACE, Wind, STEREO, Ulysses, Voyagers, IBEX, N. Horizons, Juno).
•    Plasma Physics: 1) Theoretical methods in plasma physics; 2) Statistical mechanics & thermodynamics of plasmas; 3) Basic plasma theory; 4) Plasma processes; 5) Plasma - Magnetic field coupling; 6) Quantization in plasmas.
•    Solar Physics: Modeling sunspot evolution; Magnetic flux in active regions; flares, CMEs, eruptive events.
•    Nonlinear dynamics & Complexity: 1) Difference equations (stability, phase-space diagrams, statistics of orbits; bifurcation diagrams; order & chaos in bifurcation diagrams, Lyapunov and rotation numbers, fully developed chaos, etc.); 2) Population dynamics - applications in biology (Allee effects, competition, and predator-prey models, stability, phase-space diagrams, stochastic analysis, etc.); 3) Nonlinear models of solar activity (sunspots and active regions).
•    Probability Theory & Statistics: 1) Error analysis; 2) Optimization & Fitting methods – generalization based on non-Euclidean metrics; 3) Correlation analysis; 4) Multivariate regression; 5) Heuristic methods.
•    Physical Chemistry & Biophysics: 1) Chemical kinetics; 2) Diffusion models; 3) Alzheimer’s disease; 4) Coronavirus chemistry (effect of environmental temperature on virus outbreak).

Short-Term Career Goals:

Improve understanding of the theory and applications of the following topics:
•    Theoretical development of space plasma thermostatistics.
•    Polytropic model, theory, and applications in the solar wind and other space plasmas.
•    Planck-law generalization with application in the Cosmic Microwave Background.
•    Plasma - Magnetic field coupling and energy transfer in space and astrophysical plasmas.
•    Large-scale quantization that characterizes space and astrophysical plasmas.
•    Nonlinear modeling of population dynamics.
•    General fitting methods based on Lp norms and their optimization.

Selected Publications:

See the full publication list on the publications page or at Google Scholar.