Hybrid Framework

Hybrid frameworks represent a significant trend in various scientific fields, aiming to combine the strengths of different models or approaches to overcome limitations of individual methods. Current research focuses on integrating diverse architectures, such as transformers and convolutional neural networks, spiking neural networks and traditional artificial neural networks, or data-driven and knowledge-based methods, to improve efficiency, accuracy, and robustness in applications ranging from image processing and natural language processing to robotics and multiphysics simulations. This interdisciplinary approach is driving advancements in numerous domains by leveraging the unique capabilities of each component for enhanced performance and addressing challenges like catastrophic forgetting in continual learning or efficient resource allocation in cloud computing.