Physical Neural Network

Physical neural networks (PNNs) leverage the computational capabilities of physical systems to perform machine learning tasks, aiming for improved energy efficiency and speed compared to traditional digital approaches. Current research focuses on developing effective training methods, including backpropagation-based and backpropagation-free algorithms, and exploring diverse PNN architectures based on optical, mechanical, and other physical phenomena, often incorporating physical constraints or models into the network design. This field holds significant promise for advancing artificial intelligence by enabling larger-scale, more energy-efficient models and potentially leading to novel applications in areas such as weather forecasting, materials science, and robotics.