PaRO-DeepONet: a particle-informed reduced-order deep operator network for Poisson solver in PIC simulations

Abstract

Particle-in-Cell (PIC) simulations are widely used for modeling plasma kinetics by tracking discrete particle dynamics. However, their computational cost remains prohibitively high, due to the need to simulate large numbers of particles to mitigate statistical noise and the inefficiency in handling complex geometries. To address these challenges, we propose PaRO-DeepONet, a particle-informed reduced-order surrogate framework that integrates Proper Orthogonal Decomposition (POD) with Deep Operator Network (DeepONet). By performing manifold sampling of particle evolution in PIC simulations and constructing snapshot matrices from various particle states, PaRO-DeepONet applies POD projection to extract latent charge density features that are shared across similar plasma scenarios. These reduced features are fed into the branch network of DeepONet, enabling efficient mapping from charge distributions to electrostatic potential fields. We validate PaRO-DeepONet on four representative benchmark cases. The model consistently achieves relative L2 errors below 3.5%, with the best case reaching as low as 0.82%. Notably, although the charge deposition becomes highly discrete due to particle sparsity, PaRO-DeepONet still reconstructs smooth and continuous potential fields, demonstrating strong robustness to sparse inputs and excellent generalization beyond traditional grid-based solvers. In terms of computational performance, PaRO-DeepONet reduces total PIC simulation time by 68%-85% and cuts the Poisson solver runtime by up to 99.6%. By significantly reducing computational cost while maintaining high accuracy, this work establishes machine learning-based operator surrogates as a promising new paradigm for next-generation kinetic plasma simulations.