A physics-informed deep operator network for modeling of atmospheric RF plasmas

Wenkai Li, Kun Sun, Yuantao Zhang

Abstract Fast surrogate modeling of atmospheric radio-frequency (RF) plasma fluid systems is useful for accelerating parameter scans and supporting rapid system design. However, traditional discretization methods, such as finite difference or finite element methods, remain computationally expensive when repeated evaluations are required under multiple discharge conditions. In this work, a Physics-Informed Deep Operator Network (PI-DeepONet) is developed and assessed using a simplified one-dimensional fluid model of atmospheric RF plasmas as a proof-of-principle benchmark. In the present architecture, the network is conditioned on voltage-specific (300–500 V) initial electron and ion density profiles, rather than on the applied voltage amplitude as an explicit scalar input. Therefore, for each voltage-conditioned prediction, the corresponding initial density profiles must be supplied to the network. Given these externally supplied initial profiles, a single trained model can predict plasma discharge dynamics across the entire spatiotemporal domain, including electron and ion densities, particle fluxes, electric potential, and electric field distributions. Numerical verification against finite-difference results shows that the global relative L 2 errors of all benchmark fields are below 2.1%, while the inference time is reduced by more than 300 times compared with the reference solver. Region-bounded error analysis further shows that the sheath-region relative L 2 errors remain below 5.0%, with the largest localized discrepancies occurring near the sheath and electrode boundaries. Furthermore, once the required voltage-specific initial profiles are available, the trained network can rapidly reconstruct the current–voltage characteristic curve without rerunning the full fluid solver for each voltage case. These results demonstrate that the proposed PI-DeepONet can serve as an efficient surrogate for the prescribed one-dimensional fluid model, enabling rapid voltage-parameter scans while preserving consistency with the governing equations used in the present model.