TurPy: a physics-based and differentiable optical turbulence simulator for algorithmic development and system optimization

Optical turbulence degrades free-space optical systems and is costly to model accurately. Here, we present TurPy, a high-fidelity, differentiable wave optics simulator for generating synthetic turbulence data. Using a memory-efficient split-step autoregressive algorithm with compressed phase screens, TurPy captures key atmospheric effects like wind, convection, and temporal correlation. It connects seamlessly to deep learning workflows to train downstream algorithms as well as enabling end-to-end optimization of system parameters via gradient-based methods. Validated against theoretical models with over 95% accuracy, TurPy also automates phase screen placement and demonstrates preliminary wavefront optimization. This tool bridges physics-based simulation and AI to enhance data generation, system design, and optical performance under turbulence.