Modeling Space-Dependent Plasmas with Deep Neural Network Surrogate Models
Application ID: 144861
Electron transport in low-temperature plasmas depends strongly on the electron energy distribution function (EEDF), which is often approximated as Maxwellian but is frequently nonequilibrium in reality. Incorporating nonequilibrium EEDF behavior into spatially dependent models often requires precomputed multidimensional lookup tables or the self-consistent solution of the Boltzmann equation during the simulation. This model uses a more efficient alternative: a deep neural network surrogate model trained on Boltzmann equation solutions. This enables accurate integration of kinetic effects into fluid plasma simulations while significantly reducing computational cost.
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