Near-Equilibrium Propagation Training in Nonlinear Wave Systems

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 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 internode 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 learning in physical systems in which system control is restricted to local parameters.