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

2026-04-08

AI Summary

This work addresses the challenge of end-to-end optimization in free-space optical system design, which has been hindered by the lack of high-fidelity, differentiable turbulence simulation tools. To this end, we introduce TurPy—the first differentiable, physics-informed simulator for optical turbulence across multiple media. TurPy integrates subharmonic phase screen generation, autoregressive temporal evolution, and an adaptive screen placement strategy, enabling accurate modeling of light propagation through atmospheric, oceanic, and biological environments. Leveraging GPU-accelerated wavefront propagation and parameterized power spectral density models, TurPy achieves 98% accuracy in simulating second-order Gaussian beam broadening and fourth-order plane wave scintillation under both weak and strong turbulence regimes. Furthermore, it successfully enables gradient-based optimization of a dual-domain diffractive neural network, reducing intensity scintillation at the receiver by over 20-fold.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

Developing optical systems for free-space applications requires simulation tools that accurately capture turbulence-induced wavefront distortions and support gradient-based optimization. Here we introduce TurPy, a GPU-accelerated, fully differentiable wave optics turbulence simulator to bridge high fidelity simulation with end-to-end optical system design. TurPy incorporates subharmonic phase screen generation, autoregressive temporal evolution, and an automated screen placement routine balancing Fourier aliasing constraints and weak-turbulence approximations into a unified, user-ready framework. Because TurPy's phase screen generation is parameterized through a media-specific power spectral density, the framework extends to atmospheric, oceanic, and biological propagation environments with minimal modification. We validate TurPy against established atmospheric turbulence theory by matching 2nd order Gaussian beam broadening and 4th order plane wave scintillation to closed-form models with 98% accuracy across weak to strong turbulence regimes, requiring only the medium's refractive index structure constant and power spectral density as inputs. To demonstrate TurPy as a gradient-based training platform, we optimize a dual-domain diffractive deep neural network (D2NN) in a two-mask dual-domain architecture to recover a Gaussian beam from a weakly turbulent path and achieving over 20x reduction in scintillation relative to an uncompensated receiver in simulation. TurPy is released as an open-source package to support synthetic data generation, turbulence-informed algorithm development, and the end-to-end design of optical platforms operating in turbulent environments.

Problem

• optical turbulence
• wavefront distortion
• gradient-based optimization
• free-space optics
• differentiable simulation

Innovation

• differentiable simulation
• optical turbulence
• phase screen generation
• gradient-based optimization
• wave optics