Effect of physics regularization on generative adversarial training for fast prediction of yawed wind turbine wakes

Accurate modeling of yawed turbine wakes is essential for wind farm operation and control. Analytical wake models are computationally efficient but rely on simplified assumptions, whereas high-fidelity simulations capture complex flows at a high computational cost. Purely data-driven surrogates can be effective when sufficient data are available, but they disregard governing physical laws. This study investigates the training of a physics-informed generative surrogate framework for wake modeling in which a Deep Convolutional Conditional Generative Adversarial Network (DC-cGAN) is regularized by Reynolds-averaged Navier-Stokes (RANS) residuals. We quantify how embedding these residuals affects adversarial training behavior and improves steady-state, two-dimensional hub-height wake predictions across inflow conditions and yaw variations, resulting in physically consistent and data-efficient surrogates. To support physical fidelity, the underlying RANS simulations used for data generation were first benchmarked against field observations. Subsequently, the surrogate was trained and validated against these simulation targets, confirming its ability to capture key wake characteristics in both near- and far-wake regions. Within the tested parameter range, and over six independent training runs, the proposed framework achieves median test set 𝑅2 scores of 0.987 for streamwise velocity and 0.994 for spanwise velocity, median test set mean absolute relative error of 4.605%, and a minimum inference time of 0.0085 s per turbine while outperforming other machine learning baselines in wake flow field prediction. Furthermore, integrating physics‐based regularization improves training stability, reduces prediction error variability, and maintains predictive performance under the reduced-data and out-of-distribution settings examined in this study.