IFS Seminar

In this talk I will present our research program[0] that aims to build computational tools to understand the behavior of plasmas, from tokamak edge turbulence, to the plasma environment around planets, black-holes and neutron stars. For this, we have developed solvers and new extensions to (gyro)kinetic and multi-fluid models, including special and general-relativistic effects. For magnetized plasmas I will focus on two asymptotic models: the well-known gyrokinetic model, and the recently developed Parallel-Kinetic Perpendicular Moment[1] (PKPM) model. I will present results of applying the gyrokinetic solver to turbulence in the edge region of the D3D and TCV tokamaks, comparing positive and negative triangularity discharges. I will then show application of our kinetic and fluid solvers to understand fundamental processes of reconnection and current-sheet formation in planetary magnetospheres. Finally, I will describe our recent work in new approaches to special and general relativistic plasma multi-fluids[2] to properly simulate the extreme environments around black-holes and neutron stars[3]. I will conclude with a brief outline of a novel, "tetrads-first", non-canonical Hamiltonian formulation of general relativistic kinetics, and very briefly mention the role of machine-learning in plasma physics[4,5].

 

[0] https://gkeyll.readthedocs.io/en/latest/

[1] J. Juno J., A. Hakim, J. TenBarge. (2025). “A parallel-kinetic-perpendicular-moment model for magnetised plasmas”, Journal of Plasma Physics 91, 5:E129

[2] J. Gorard, A. Hakim, J. Juno, J. TenBarge (2024). “A Tetrad-First Approach to Robust Numerical Algorithms in General Relativity”, arXiv:2410.02549

[3] Gorard, J., Juno, J., Hakim, A. (2025). “Hydrodynamic and Electromagnetic Discrepancies between Neutron Star and Black Hole Spacetimes”, Physical Review Letters (submitted); arXiv:2505.05299.

[4] J. Gorard, A. Hakim, A. (2025). “Shock with Confidence: Formal Proofs of Correctness for Hyperbolic Partial Differential Equation Solvers”, arXiv:2503.13877.
[5] Nick McGreivy & Ammar Hakim. (2024) "Weak baselines and reporting biases lead to overoptimism in machine learning for fluid-related partial differential equations", Nature Machine Intelligence, 6, 1256–1269

 

Ammar Hakim is a Principal Research Physicist and the Deputy Head of the Computational Sciences Department at Princeton Plasma Physics Laboratory. His research interests span all aspects of computational & theoretical plasma physics.