Physics-informed neural networks (PINNs) have a wide range of applications as an
alternative to traditional numerical methods in plasma simulation. However, in some specific …

The physics-informed neural network (PINN) is able to identify partial differential equation
(PDE) coefficients which are constant across the space directly from physical …

Spark is a widely studied plasma source for active species production; however, it
experiences unstable transitions (eg to a thermal arc) at high frequencies or long pulse …

Electromagnetic (EM) wave propagation and inversion in complex time-varying medium is a
challenging problem, particularly for plasma applications. We extend the EM wave–plasma …

This communication presents a novel end-to-end artificial neural network (ANN)-based 2-D
electromagnetic (EM) full-wave inversion (FWI) scheme in which the transceiver …

Kinetic approaches are generally accurate in dealing with microscale plasma physics
problems but are computationally expensive for large-scale or multiscale systems. One of …

This work presents a deep neural network (DNN)-based approach for identifying the modal
field distributions of closed non-radiating waveguides. Specifically, physics-informed neural …

Simulations of high-energy density physics often need non-local thermodynamic equilibrium
opacity data. These data, however, are expensive to produce at relatively low fidelity. It is …

We report observations of stimulated electromagnetic emission (SEE) induced by high
power high frequency (HF) radio waves near the third electron gyroharmonic (3) at …

Solving Maxwell's equations is crucial in various fields, like electromagnetic scattering and
antenna design optimization. Physics-informed neural networks (PINNs) have shown …