Adaptive Stepping PTA for DC Analysis Based on Reinforcement Learning

Solving the DC operating point efficiently for large-scale nonlinear circuit is crucial and quite challenging. Pseudo transient analysis (PTA) is a widely-used and promising DC solver in the industry, in which the stepping policy is of great importance for PTA convergence and simulation efficiency. In this brief, a reinforcement learning (RL)-enhanced stepping policy is proposed. It designs dual Actor-Critic agents with stochastic policy and online adaptive scaling to intelligently evaluate PTA convergence status, and adaptively adjust forward and backward time-step size. Numerical examples demonstrate that a significant efficiency speedup and convergence improvement over the previous stepping methods is achieved by the proposed RL-enhanced stepping policy.