Hybrid and Deep Neural Network Models for STEM Applications

Recent advances in data availability, computational resources, and artificial intelligence have significantly influenced research and development across science, technology, engineering, and mathematics (STEM). Deep neural networks (DNNs) have developed as effective tools for demonstrating complicated, nonlinear, and multidimensional systems. Data-driven nature often limits interpretability, data efficiency, and adherence to governing physical principles. To overcome these limitations, hybrid neural network models integrate deep learning with complementary approaches such as classical machine learning, fuzzy logic, symbolic reasoning, evolutionary optimization, and physics-based modeling. This chapter gives a systematic study of deep and hybrid neural network architectures relevant to STEM applications. It introduces the fundamental concepts of deep neural networks, discusses major learning architectures and optimization strategies, and examines prominent hybrid frameworks including CNN-RNN hybrids, neuro-fuzzy systems, neuro-symbolic models, and physics-informed neural networks. Furthermore, the chapter reviews representative applications in environmental science, biomedical engineering, material science, computational physics, and engineering systems such as predictive maintenance, intelligent control, smart manufacturing, and cyberphysical systems. Integrating empirical data with domain expertise and physical restrictions, hybrid neural network models demonstrate improved robustness, generalization, and reliability. The chapter concludes by highlighting the role of hybrid learning frameworks as key enablers for scalable and trustworthy solutions in modern STEM research and engineering practice.