Digital twin-enabled neural networks will develop innovative processes in feature selection and simulation. In addition, this methodology will have development in autonomous driving, natural language processing, healthcare, and many other fields. Recently sensors have been widely used for environment monitoring, and massive data has to be processed efficiently and effectively, which requires managed neural architectures for sustainable computing. The sustainable digital twin-empowered architectures create new biological evolution simulation algorithms and intelligent system architectures for supervised and unsupervised learning. Some of today's fundamental artificial intelligence issues, including adaptive machine learning and neuromorphic cognitive models, can be overcome by this methodology. The goals of this special issue on digital twin-enabled neural network architecture management for sustainable computing aim to pay attention to the researchers and industries towards recent advances in decision-making algorithms, neural network models and architectures for faster processing.
