A specialized PINN architecture solves the spatially inhomogeneous electron Boltzmann equation with high accuracy across gases and electric field strengths without case-specific tuning.
org/10.1088/0963-0252/14/4/011
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
fields
physics.plasm-ph 3verdicts
UNVERDICTED 3representative citing papers
Monte Carlo method simulates electron avalanches with feedback to estimate discharge inception probability and time lag per initial electron position across 2D and 3D electrode geometries.
Temporal atomic oxygen density and dissociation dynamics measured in a micro-cavity DBD via multi-PMT OES and validated with a 0-D model show up to 100% dissociation in He/O2 mixtures.
citing papers explorer
-
A physics-informed neural network approach to solve the spatially inhomogeneous electron Boltzmann equation
A specialized PINN architecture solves the spatially inhomogeneous electron Boltzmann equation with high accuracy across gases and electric field strengths without case-specific tuning.
-
Stochastic simulation of partial discharge inception
Monte Carlo method simulates electron avalanches with feedback to estimate discharge inception probability and time lag per initial electron position across 2D and 3D electrode geometries.
-
Analyzing atomic oxygen product evolution in Micro Cavity Plasma Arrays by a combination of a Multi-PMT OES Setup and a 0-D Chemical Model
Temporal atomic oxygen density and dissociation dynamics measured in a micro-cavity DBD via multi-PMT OES and validated with a 0-D model show up to 100% dissociation in He/O2 mixtures.