Multi-Scale Shock Wave Mechanics: Experimental, Computational, and AI Perspectives offers a comprehensive exploration of shock wave phenomena across multiple scales by integrating fundamental theory, high-fidelity computational modeling, advanced experimental diagnostics, and artificial intelligence techniques. This volume bridges classical shock wave physics with data-driven methodologies, providing a unified framework for understanding and predicting complex shock-driven processes in contemporary science and engineering.
Structured into five cohesive parts, the book establishes theoretical and mathematical foundations, advances to experimental investigations with modern diagnostic techniques, and focuses on computational modeling including high-resolution simulations and instability analysis. A dedicated section explores AI's transformative role, demonstrating how machine learning and physics-informed neural networks enhance predictive accuracy and real-time analysis. The final part addresses practical applications in hypersonic aerospace systems. Combining rigorous formulations, computational tools, and AI innovations, this book provides insights into multi-scale shock dynamics, turbulence–shock interactions, and high-speed flow phenomena.
This volume serves as an essential resource for researchers, academicians, and industry professionals in aerospace engineering, computational fluid dynamics, applied mathematics, and plasma physics. It consolidates current advances and offers a visionary outlook on future research directions in shock wave physics and AI-enhanced predictive modeling.