My research interests focus on the study of equilibrium, stability, and dynamics of laboratory and astrophysical plasmas using Hamiltonian fluid dynamics, kinetic and hybrid models, as well as neural networks and machine learning methods. I am particularly interested in the Hamiltonian construction of new hybrid fluid-kinetic models characterized by consistent conservation laws that have applications in the study of magnetic confinement of plasma in fusion reactors such as the tokamak and in phenomena related to astrophysical plasma like magnetic reconnection. I am also interested in the application of computational methods that obey conservation laws and deep learning methods to solve the differential equations arising in these models.

Research interests and activities:

• Plasma Physics

• Plasma Kinetic and Fluid Theory 

• Hamiltonian Dynamics

• Partial Differential Equations

• Numerical Methods for PDEs

• Equation-driven Machine Learning

• Physics Informed Neural Networks

Contributions: 

• Equilibrium and stability analysis of generalized MHD models via Hamiltonian variational principles. 

• Novel analytic and numerical solutions to generalized Grad-Shafranov Equations.

• Novel hybrid Vlasov dynamical and equilibrium models.

• Physics Informed Neural Networks

Current research projects:

• Hamiltonian description of fluid-kinetic and generalized MHD models

• Kinetic and hybrid-kinetic equilibria

• Artificial neural networks for PDEs

• Tokamak, coronal and magnetospheric plasma equilibria