Bridging Physics and AI

Second edition expanded to reflect the field's rapid evolution. Introduces scientific machine learning, physics-informed neural networks, generative, causal inference, AI-driven discovery pipelines, the growing importance of big data, computational infrastructure, and ethical considerations in scientific practice. Bridging Physics and AI: Data Science, Machine Learning, and Computational Modelling A unifying framework for understanding how physics and artificial intelligence converge to drive the future of scientific discovery. This book presents a coherent and integrated approach to physics and artificial intelligence, demonstrating how fundamental principles—dynamics, energy, uncertainty, and symmetry—extend naturally into modern machine learning and data science. Grounding AI methods in physical intuition enables readers to move seamlessly from theory to application. Spanning foundational concepts to advanced topics such as generative models, causal inference, and high-performance computing, the text equips readers with both the conceptual insight and practical tools needed to model complex systems, design efficient experiments, and extract meaning from high-dimensional data. The second edition has been significantly expanded to reflect the field's rapid evolution. It introduces scientific machine learning, physics-informed neural networks (PINNs), generative models (VAEs, GANs, diffusion models), causal inference, and AI-driven discovery pipelines, while also addressing the growing importance of big data, computational infrastructure, and ethical considerations in scientific practice. Designed for students, researchers, and professionals, Bridging Physics and AI offers a rigorous yet accessible pathway into one of the most transformative interdisciplinary domains of our time—empowering readers not only to apply these tools, but to understand and shape their future development. Key Features Physics-first approach to artificial intelligence and machine learning Clear connections between energy landscapes and optimization, and dynamical systems and learning algorithms Comprehensive coverage of modern AI techniques—including generative models and AI-driven discovery—within a rigorous scientific framework Strong emphasis on practical application, intuition, and conceptual clarity Designed for both self-learning and classroom instruction Integrated treatment of ethics, interpretability, and trust in scientific AI Coverage of high-performance computing and big data in scientific contexts Includes comprehensive appendices on computational tools and pedagogy

Pagina's: 328, Editie: 2, Paperback, CRC Press