LAUSR

In recent years, non-thermal plasma technology has emerged as one of the most promising candidates for decomposing [Formula: see text]. The fluid model, a powerful tool to investigate the plasma dynamics, is computationally costly in simulating complex [Formula: see text] plasma with tens of particles and hundreds of reactions, especially driven by short pulsed voltages. In this paper, a deep neural network (DNN) is proposed to describe the discharge characteristics and plasma chemistry of [Formula: see text] pulsed discharge at atmospheric pressure. The DNN is trained using the simulation data obtained from the fluid model and then continuously optimized by minimizing the loss function. The effectiveness and feasibility of the DNN are verified by comparing with the experimental measurement and the numerical simulation results. Compared to the time-consuming fluid simulations with tens of hours, the well-trained DNN typically requires only a few seconds to obtain the essential characteristics of [Formula: see text] pulsed discharges with high accuracy, significantly improving the computational efficiency. The DNN prediction results show that increasing the pulse rise rate at a given voltage amplitude can effectively raise the discharge current and breakdown voltage, and the electric field in the sheath region also increases with the pulse rise rate. In addition, the density of the surface charge accumulated on the dielectric layer increases with the plateau duration, and then a strong induced electric field by the surface charges is established, which obviously improves the discharge current during the pulse fall phase. The predicted data also show that increasing the pulse rise rate and the plateau duration could effectively improve the density of product species, such as CO and [Formula: see text], leading to an increase in [Formula: see text] conversion. This study demonstrates that the DNN method is a reliable tool for obtaining the essential discharge characteristics of atmospheric [Formula: see text] pulsed plasma and provides a promising avenue for future applications of DNN-based methods in non-thermal plasmas.