LBMNet for Natural Language Processing (NLP) 

LBMNet for Natural Language Processing (NLP) is a physics-inspired neural architecture that extends the principles of the Lattice Boltzmann Method to language understanding and generation. By combining multi-scale LBM encoding, moment-based feature extraction, and attention mechanisms, the network effectively captures local and global linguistic dependencies. Its hierarchical design enables efficient representation learning for a wide range of NLP tasks, including text classification, machine translation, sentiment analysis, question answering, and next-word prediction.