This book offers a comprehensive survey of recent progress in hybrid neural networks, combining theoretical foundations, methodological advances, and application driven research across diverse scientific and engineering domains. Neural networks are increasingly advancing beyond purely data driven paradigms toward hybrid approaches that integrate machine learning with physical laws, domain constraints, causal reasoning, and human expertise. The chapters included in the bookoriginate from peer reviewed contributions presented at an international conference and two specialized workshops focused on artificial intelligence for civil engineering, industrial systems, and business applications. Collectively, the authorsexamine how hybrid neural models improve interpretability, robustness, fairness, and generalization-capabilities essential for deploying AI in safety critical and high impact environments. Methodologically, the bookcovers a wide range of hybrid learning paradigms, including physics informed neural networks, hybrid multi scale architectures, graph neural networks, transformer based and vision models, and entropy guided analyses of deep learning systems. Additional chapters explore learning strategies grounded in causal principles, physically inspired regularization, and human in the loop optimization. A substantialportion of the book is devoted to real world applications. In engineering and the physical sciences, the bookincludes studies on seismic monitoring, structural and architectural optimization, synthetic data generation, smart energy management, load disaggregation, and atmospheric retrieval for exoplanet investigation. Further chapters examine economic, social, and industrial use cases, including financial forecasting, insurance and sales analytics, fairness aware decision support systems, social well being estimation, and AI enhanced manufacturing aligned with Industry 5.0. By unifying theoretical principles, computational models, and practical ap
