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Researchers from the School of Engineering at the University of Utah have published a groundbreaking study in Nature Communications, introducing a novel technology that harnesses the power of the Optical Neural Engine (ONE) to efficiently tackle partial differential equations. This innovative approach leverages the unique properties of optical waves to encode equation information, seamlessly integrating optical matrix multipliers and diffractive optical neural networks for swift, energy-efficient computations. Tests have demonstrated ONE's exceptional performance in solving a wide array of classic partial differential equations, opening up new horizons in scientific computing and engineering simulations. This technology is poised to propel advancements in diverse fields, including geological modeling, chip design, and climate modeling.
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