Title: PD-FS:Surrogate-Enhanced Physical Data-Driven Framework for Rapid Deep Reinforcement Learning Control
Primary area: learning on time series and dynamical systems
Keywords: Deep reinforcement learning, Bionic propulsion, Surrogate model, Physical Partition Network, Computational Fluid Dynamics

Abstract: While deep reinforcement learning (DRL) has demonstrated broad potential in sequential decision-making, its application to fluid–dynamic systems remains limited by the prohibitive cost of high-fidelity simulations and the difficulty of capturing multi-scale unsteady behaviors. We are the first to put forward a Physical Data-Driven Flow Simulation (PD-FS) framework, a staged pipeline that couples lightweight neural surrogates with physics-guided refinement in full-order CFD. PD-FS incorporates mode-conditioned surrogate models with cycle-locked and memory-aware updates, enabling sample-efficient training while faithfully reproducing critical frequency-switching dynamics. Policies refined in CFD solvers adapt to nonlinear flow responses without relying on extensive domain randomization. Benchmarked on canonical unsteady flow-control tasks, PD-FS achieves nearly 50 faster training compared with CFD baselines, while reducing energy expenditure by over 20% at comparable success rates. These results highlight PD-FS as a versatile framework that unifies surrogate efficiency with CFD fidelity, offering a scalable tool for reinforcement learning–based control in complex fluid–dynamic environments.