Physics-aware machine learning integrates domain-specific physical knowledge into machine learning models, leading to the development of physics-informed neural networks (PINNs). PINNs embed physical ...

Continual learning (CL), the ability to learn new tasks without forgetting existing ones, is one of the greatest challenges in AI. Our work provides an analytically tractable theory that captures some ...

Abstract: A sophisticated hybrid quantum convolutional neural network (HQCNN) is conceived for handling the pilot assignment task in cell-free massive MIMO systems, while maximizing the total ergodic ...

Abstract: Recent advances in hardware and software technology have made it possible to implement more resourcedemanding deep learning algorithms in constrained hardware environments. This creates ...

Neural networks have emerged as powerful tools for predicting key quality attributes of yarn, including tensile strength, evenness, density irregularities and functional performance. By capturing ...