6 papers
Imposing quasineutrality on electrostatic plasmas via the Dirac theory of constraints
D. A. Kaltsas, J. W. Burby, P. J. Morrison +2
We present a method for imposing quasineutrality and, more generally, charge density conservation in the Vlasov-Poisson (VP) and Vlasov-Ampère (VA) systems, which describe electro…
Hamiltonian formulation of the quasineutral Vlasov-Poisson system
J. W. Burby, D. A. Kaltsas, P. J. Morrison +2
Slow manifold reduction and the theory of Poisson-Dirac submanifolds are used to deduce a Hamiltonian formulation for a quasineutral limit of the planar, collisionless, magnetized…
Hybrid fluid-kinetic cylindrical equilibria with axial background magnetic field
D. A. Kaltsas, A. I. Kuiroukidis, G. N. Throumoulopoulos
Self-consistent, one-dimensional quasineutral screw-pinch equilibria are constructed within a hybrid model that couples fluid electrons with kinetic ions governed by the Vlasov equ…
Multi-soliton solutions and data-driven discovery of higher-order Burgers' hierarchy equations with physics informed neural networks
D. A. Kaltsas, L. Magafas, P. Papadopoulou +1
The Burgers hierarchy consists of nonlinear evolutionary partial differential equations (PDEs) with progressively higher-order dispersive and nonlinear terms. Notable members of th…
New axisymmetric equilibria with flow from an expansion about the generalized Solov'ev solution
A. I. Kuiroukidis, D. A. Kaltsas, G. N. Throumoulopoulos
We construct analytic solutions to the generalized Grad-Shafranov equation, which incorporates both toroidal and poloidal flows. This is achieved by adopting a general linearizing…
Constrained Hamiltonian Systems and Physics-Informed Neural Networks: Hamilton-Dirac Neural Networks
Dimitrios A. Kaltsas
The effectiveness of the Physics Informed Neural Networks (PINNs) for learning the dynamics of constrained Hamiltonian systems is demonstrated using the Dirac theory of constraints…