Li Shuaifeng, Mao Xiaoming
Department of Physics, University of Michigan, Ann Arbor, 48109, MI, USA.
Nat Commun. 2024 Dec 9;15(1):10528. doi: 10.1038/s41467-024-54849-z.
Recent advances unveiled physical neural networks as promising machine learning platforms, offering faster and more energy-efficient information processing. Compared with extensively-studied optical neural networks, the development of mechanical neural networks remains nascent and faces significant challenges, including heavy computational demands and learning with approximate gradients. Here, we introduce the mechanical analogue of in situ backpropagation to enable highly efficient training of mechanical neural networks. We theoretically prove that the exact gradient can be obtained locally, enabling learning through the immediate vicinity, and we experimentally demonstrate this backpropagation to obtain gradient with high precision. With the gradient information, we showcase the successful training of networks in simulations for behavior learning and machine learning tasks, achieving high accuracy in experiments of regression and classification. Furthermore, we present the retrainability of networks involving task-switching and damage, demonstrating the resilience. Our findings, which integrate the theory for training mechanical neural networks and experimental and numerical validations, pave the way for mechanical machine learning hardware and autonomous self-learning material systems.
最近的进展揭示了物理神经网络是有前途的机器学习平台，能提供更快且更节能的信息处理。与经过广泛研究的光学神经网络相比，机械神经网络的发展仍处于初期，面临重大挑战，包括繁重的计算需求以及使用近似梯度进行学习。在此，我们引入原位反向传播的机械模拟，以实现机械神经网络的高效训练。我们从理论上证明可以在局部获得精确梯度，从而通过紧邻区域进行学习，并且我们通过实验展示了这种反向传播能够高精度地获得梯度。利用梯度信息，我们在行为学习和机器学习任务的模拟中展示了网络的成功训练，在回归和分类实验中实现了高精度。此外，我们展示了涉及任务切换和损伤的网络的可再训练性，证明了其弹性。我们的研究结果整合了训练机械神经网络的理论以及实验和数值验证，为机械机器学习硬件和自主自学习材料系统铺平了道路。
