Data-driven discovery of reduced plasma physics models from fully-kinetic simulations

Abstract

At the core of some of the most important problems in plasma physics -- from controlled nuclear fusion to the acceleration of cosmic rays -- is the challenge to describe nonlinear, multi-scale plasma dynamics. The development of reduced plasma models that balance between accuracy and complexity is critical to advancing theoretical comprehension and enabling holistic computational descriptions of these problems. Here, we report the data-driven discovery of accurate reduced plasma models, in the form of partial differential equations, directly from first-principles particle-in-cell simulations. We achieve this by using an integral formulation of sparsity-based model-discovery techniques and show that this is crucial to robustly identify the governing equations in the presence of discrete particle noise. We demonstrate the potential of this approach by recovering the fundamental hierarchy of plasma physics models -- from the Vlasov equation to magnetohydrodynamics. Our findings show that this data-driven methodology offers a promising new route to accelerate the development of reduced theoretical models of complex nonlinear plasma phenomena and to design computationally efficient algorithms for multi-scale plasma simulations.

References in corpus (6)

• Particle acceleration in relativistic collisionless shocks: Fermi process at last?
• One-to-one direct modeling of experiments and astrophysical scenarios: pushing the envelope on kinetic plasma simulations
• Using Noisy or Incomplete Data to Discover Models of Spatiotemporal Dynamics
• Nonlinear growth of firehose and mirror fluctuations in turbulent galaxy-cluster plasmas
• On magnetic field amplification and particle acceleration near non-relativistic astrophysical shocks: Particles in MHD Cells simulations
• Numerical heating in particle-in-cell simulations with Monte Carlo binary collisions

Cited by in corpus (21)

• Ensemble-SINDy: Robust sparse model discovery in the low-data, high-noise limit, with active learning and control
• Data-driven discovery of active nematic hydrodynamics
• Data-driven modeling of Landau damping by physics-informed neural networks
• Learning fluid physics from highly turbulent data using sparse physics-informed discovery of empirical relations (SPIDER)
• Machine-learning heat flux closure for multi-moment fluid modeling of nonlinear Landau damping
• In Search of a Data Driven Symbolic Multi-Fluid 10-Moment Model Closure
• Identification of high order closure terms from fully kinetic simulations using machine learning
• Data-driven Discovery of Delay Differential Equations with Discrete Delays
• Data-driven discovery of a heat flux closure for electrostatic plasma phenomena
• Influence of initial conditions on data-driven model identification and information entropy for ideal mhd problems
• Promoting global stability in data-driven models of quadratic nonlinear dynamics
• Data-driven sparse modeling of oscillations in plasma space propulsion
• Accelerating Particle-in-Cell Kinetic Plasma Simulations via Reduced-Order Modeling of Space-Charge Dynamics using Dynamic Mode Decomposition
• Learning the dynamics of a one-dimensional plasma model with graph neural networks
• Dynamics of tidal spiral arms: Machine learning-assisted identification of equations and application to the Milky Way
• Surrogate Modeling of Landau Damping with Deep Operator Networks
• Electron neural closure for turbulent magnetosheath simulations: energy channels
• Sparse identification of effective microparticle interaction potential in dusty plasma from simulation data
• Embedding physical symmetries into machine-learned reduced plasma physics models via data augmentation
• The Machine Learning Approach to Moment Closure Relations for Plasma: A Review
• Optimal Landau-type closure parameters for two-fluid simulations of plasma turbulence at kinetic scales