Daniel Plummer

Abstract:

<jats:p>We present a data-driven framework for the modeling of nonlocal heat transport in plasmas using a nonlocal theory-informed neural network trained on kinetic particle-in-cell simulations that span both local and nonlocal regimes. The model learns spatio-temporal heat flux kernels directly from simulation data, capturing dynamic transport behaviors beyond the reach of classical formulations. Unlike time-independent kernel models such as Luciani-Mora-Virmont and Schurtz-Nicolaï-Busquet models, our approach yields physically grounded, time-evolving kernels that adapt to varying plasma conditions. The resulting predictions show strong agreement with kinetic benchmarks across regimes. This offers a promising direction for data-driven modeling of nonlocal heat transport and contributes to a deeper understanding of plasma dynamics.</jats:p>

Abstract:

We present a novel scheme for modelling quantum plasmas in the warm dense matter (WDM) regime via a hybrid smoothed particle hydrodynamic - molecular dynamic treatment, here referred to as ‘Bohm SPH’. This treatment is founded upon Bohm’s interpretation of quantum mechanics for partially degenerate fluids, does not apply the Born-Oppenheimer approximation, and is computationally tractable, capable of modelling dynamics over ionic timescales at electronic time resolution. Bohm SPH is also capable of modelling non-Gaussian electron wavefunctions. We present an overview of our methodology, validation tests of the single particle case including the hydrogen 1s wavefunction, and comparisons to simulations of a warm dense hydrogen system performed with wave packet molecular dynamics.