光学神经引擎高效求解偏微分方程 为下一代高性能计算技术发展开辟新方向

Core Insights - A groundbreaking study from the University of Utah's College of Engineering introduces a method to encode partial differential equations (PDEs) into light waves, processed by a novel optical device called the Optical Neural Engine (ONE), marking a significant step from theoretical exploration to practical application in optical computing [1][2]. Group 1: Technology and Methodology - The ONE system combines the advantages of diffractive optical neural networks and optical matrix multipliers, modeling PDEs using optical methods rather than traditional digital representations [1]. - The system utilizes different properties of light waves, such as intensity and phase, to represent various variables in the equations, allowing the light signals to evolve through a series of optical components to yield solutions to specific PDEs [1][2]. Group 2: Applications and Impact - The research tested the ONE system on several classical PDEs, including Darcy's law (for groundwater flow modeling), the static magnetic Poisson equation in demagnetization processes, and the Navier-Stokes equations (widely used in fluid mechanics), demonstrating good adaptability and accuracy [2]. - This research provides a multifunctional, high-efficiency platform for large-scale scientific computing and engineering simulations, with potential significant impacts in fields such as geological modeling, chip design, and climate simulation [2].