| book details |

Recent Advances in Hybrid Neural Networks: Theory, Methods, and Applications

Edited by Anna Esposito, Edited by Marcos Faundez-Zanuy, Edited by Francesco Carlo Morabito, Edited by Eros Pasero, Edited by Gennaro Cordasco

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normal price: R 7 642.95

Price: R 6 877.95

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| book description |

This book offers a comprehensive survey of recent progress in hybrid neural networks, combining theoretical foundations, methodological advances, and applicationâ€‘driven research across diverse scientific and engineering domains. Neural networks are increasingly advancing beyond purely dataâ€‘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 peerâ€‘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 generalizationâ€”capabilities essential for deploying AI in safetyâ€‘critical and highâ€‘impact environments. Methodologically, the bookcovers a wide range of hybrid learning paradigms, including physicsâ€‘informed neural networks, hybrid multiâ€‘scale architectures, graph neural networks, transformerâ€‘based and vision models, and entropyâ€‘guided analyses of deep learning systems. Additional chapters explore learning strategies grounded in causal principles, physically inspired regularization, and humanâ€‘inâ€‘theâ€‘loop optimization. A substantialportion of the book is devoted to realâ€‘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, fairnessâ€‘aware decisionâ€‘support systems, social wellâ€‘being estimation, and AIâ€‘enhanced manufacturing aligned with Industry 5.0. By unifying theoretical principles, computational models, and practical applications within a hybrid neural framework, this book highlights the growing role of neural networks as interpretable, trustworthy, and domainâ€‘aware systems. It serves as a timely reference for researchers, advanced students, and practitioners seeking to design and implement nextâ€‘generation neural solutions that bridge the gap between data, structured knowledge, and realâ€‘world decisionâ€‘making.