Title: Near-Equilibrium Propagation training in nonlinear wave systems Show Chinese title

Title: 非平衡传播训练在非线性波系统中

Abstract: Backpropagation learning algorithm, the workhorse of modern artificial intelligence, is notoriously difficult to implement in physical neural networks. Equilibrium Propagation (EP) is an alternative with comparable efficiency and strong potential for in-situ training. We extend EP learning to both discrete and continuous complex-valued wave systems. In contrast to previous EP implementations, our scheme is valid in the weakly dissipative regime, and readily applicable to a wide range of physical settings, even without well defined nodes, where trainable inter-node connections can be replaced by trainable local potential. We test the method in driven-dissipative exciton-polariton condensates governed by generalized Gross-Pitaevskii dynamics. Numerical studies on standard benchmarks, including a simple logical task and handwritten-digit recognition, demonstrate stable convergence, establishing a practical route to in-situ learning in physical systems in which system control is restricted to local parameters. Show Chinese abstract

Abstract: 反向传播学习算法是现代人工智能的核心，但在物理神经网络中实现起来非常困难。 平衡传播（EP）是一种效率相当的替代方法，并且在原位训练方面具有强大的潜力。 我们将EP学习扩展到离散和连续的复数值波系统。 与之前的EP实现不同，我们的方案适用于弱耗散区域，并且可以方便地应用于各种物理设置，即使没有明确的节点，可训练的节点间连接也可以由可训练的局部势能代替。 我们在由广义Gross-Pitaevskii动力学控制的驱动耗散激子极化子凝聚体中测试了该方法。 在标准基准测试中的数值研究，包括一个简单的逻辑任务和手写数字识别，证明了稳定的收敛性，为在系统控制仅限于局部参数的物理系统中实现原位学习提供了一条实用的路径。