发表机构
Virginia Tech; Purdue University（弗吉尼亚理工大学; 普渡大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究利用共享二阶非线性过程，在光学、微波和声波平台上以相同预训练参数实现频域物理神经网络，无需参数微调，在统一四类分类任务中分别达到97.6%、98.4%和98.2%的推理精度，为跨平台通用神经网络奠定基础。
AI 中文摘要
频域物理神经网络（PNN）已成为一种有前景的模拟计算范式，实现了器件数量减少、鲁棒性增强和高推理精度。然而，现有在光学、声学和电子学等不同物理领域的演示大多针对特定器件或平台，通常需要依赖硬件的模型和制造后参数调优。在此，我们展示了在不同波基计算平台上使用相同的预训练参数实现频域物理神经网络的跨平台部署。通过利用共享的二阶非线性过程，PNN模型在光学、微波和声波平台上实现，无需微调参数。在统一的四类分类任务上评估，频域物理神经网络在光学中达到97.6%的高推理精度，在电子学中达到98.4%，在力学中达到98.2%。此外，我们系统地比较了这三个不同物理领域的相关性能指标，为跨不同物理平台的通用神经网络铺平了道路。
英文摘要
Frequency-domain physical neural networks (PNNs) have emerged as a promising analog computing paradigm, achieving a reduced number of devices, enhanced robustness, and high inference accuracy. However, existing demonstrations across various physical domains, such as optics, acoustics, and electronics, are mostly specific to the target devices or platforms, usually requiring hardware-dependent models and post-fabrication parameter tuning. Here, we demonstrate cross-platform implementations of frequency-domain PNN across different wave-based computing platforms using identical, pre-trained parameters. By exploiting shared second-order nonlinear processes, the PNN model is implemented on optical, microwave, and acoustic-wave platforms without the need of fine tuning the parameters. Evaluated on a unified four-class classification task, the frequency-domain PNN achieves high inference accuracies of 97.6% in optics, 98.4% in electronics, and 98.2% in mechanics. Further, we systematically compare related performance metrics across these three distinct physical domains, paving the way to a universal neural network across different physical platforms.
