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DIODES: Diffractive Optical Differential Equation Solver
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DOI：10.1002/lpor.71649.png)
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Abstract
En 中文
Efficient partial differential equation (PDE) solving is central to scientific and engineering applications, and data-driven approaches have emerged as fast alternatives for large-scale problems. However, their deployment remains dominated by electronic hardware with growing energy and integration density constraints. Optical and photonic computing offers a high-throughput and energy-efficient platform, but existing optical PDE solvers remain limited by hardware complexity and scalability. Here, we present a diffractive optical differential equation solver (DIODES) architecture that combines real-space and Fourier-space optical processing in a streamlined hardware implementation built mainly from diffractive components. DIODES removes optical matrix–vector multiplier hardware and enables nonlinear optical processing using linear optics instead of electronic nonlinear modules, providing a practical route toward all-optical PDE solvers. We demonstrate DIODES on the time-independent Darcy flow equation, achieving strong predictive capability, resolution-scalable trainability, and performance comparable to representative optical and electronic models. To guide experimental implementation, we also analyze various experimental nonidealities and show that experiment-aware training improves robustness to static nonidealities. Finally, we extend DIODES to time-dependent Maxwell's equations in a dielectric metasurface example. These results establish DIODES as a streamlined optical computing framework for scalable, high-throughput, and energy-efficient PDE solving.
Keywords:
optical diffractive
optical computing
partial differential equations