Bridging Physics and AI: Data Science, Machine Learning, and Computational Modelling
Autor Ijaz A. Raufen Limba Engleză Hardback – 28 ian 2027
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
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Specificații
ISBN-13: 9781041136941
ISBN-10: 1041136943
Pagini: 328
Ilustrații: 174
Dimensiuni: 156 x 234 mm
Ediția:2
Editura: CRC Press
Colecția CRC Press
ISBN-10: 1041136943
Pagini: 328
Ilustrații: 174
Dimensiuni: 156 x 234 mm
Ediția:2
Editura: CRC Press
Colecția CRC Press
Public țintă
General, Postgraduate, Professional Reference, Undergraduate Advanced, and Undergraduate CoreCuprins
1. From Quarks to Qubits: Physics in the Age of Artificial Intelligence. 2. Classical and Statistical Mechanics Refresher. 3. Quantum Foundations and Probabilistic Representation. 4. Probability, Statistics, and Inference in Physics. 5. Experimental Design and Data Handling in Physics. 6. Machine Learning Foundations for Physics. 7. Deep Learning Architectures for Physics and Scientific Discovery. 8. Generative Models, Optimization, and AI-Driven Scientific Discovery. 9. Ethics, Interpretability, and Validation in Artificial Intelligence Models. 10. Big Data and High-Performance Scientific Computing.
Notă biografică
Dr. Ijaz A. Rauf is a physicist, data scientist, entrepreneur, and Lean Six Sigma Master Black Belt (MBB), whose career spans academia, industry, and global innovation in scientific and data-driven systems. He holds a Ph.D. in Physics from the University of Cambridge, where he specialized in experimental condensed matter physics.
Dr. Rauf began his career in academia, conducting research and teaching at leading institutions in Canada and the United States. Following nearly a decade in academic research and education, he transitioned to the high-technology industry, where he applied his expertise in physics, materials science, and statistical methods to real-world engineering and manufacturing challenges.
He has held senior technical and leadership roles in industry, including at PerkinElmer Optoelectronics, where he led process optimization and innovation initiatives using Lean Six Sigma methodologies. Over the course of his career, he has founded and co-founded multiple technology ventures, including serving as President and CEO of SolarGrid Energy Inc., and currently leads Eminent-Tech Corporation, a consulting firm specializing in data science, advanced analytics, and operational excellence.
Dr. Rauf has authored over 50 international scientific publications and holds patents in advanced materials and process engineering. His research spans predictive analytics, machine learning, nanotechnology, and thin-film materials, with applications in renewable energy, nano-biosensors, and point-of-care diagnostics. More recently, his work has focused on integrating artificial intelligence with physical systems, including the development of physics-informed models and data-driven scientific discovery frameworks.
In addition to his industrial and research contributions, Dr. Rauf has maintained a strong commitment to education. He serves as an Adjunct Professor in the Department of Physics and Astronomy at York University and has been affiliated with Toronto Metropolitan University (formerly Ryerson University) as a research associate. He has also held international academic appointments, including Visiting Professor at the University of Energy and Natural Resources in Ghana and Foreign Faculty at COMSATS Institute of Information Technology in Pakistan.
Dr. Rauf has contributed extensively to professional and public service. He has served as an appointed Council Member of the Ontario College of Teachers, Vice-Chair of the Board of Directors at Blue Hills Child and Family Services, and as a panel expert for the International Renewable Energy Agency (IRENA). Through these roles, he has supported policy development, governance, and the advancement of education and renewable energy initiatives at national and international levels.
Across academia, industry, and entrepreneurship, Dr. Rauf’s work is unified by a central theme: the application of rigorous scientific principles, statistical thinking, and machine learning to solve complex, real-world problems. His current focus lies at the intersection of physics and artificial intelligence, where he is actively contributing to the emerging field of scientific machine learning.
Dr. Rauf began his career in academia, conducting research and teaching at leading institutions in Canada and the United States. Following nearly a decade in academic research and education, he transitioned to the high-technology industry, where he applied his expertise in physics, materials science, and statistical methods to real-world engineering and manufacturing challenges.
He has held senior technical and leadership roles in industry, including at PerkinElmer Optoelectronics, where he led process optimization and innovation initiatives using Lean Six Sigma methodologies. Over the course of his career, he has founded and co-founded multiple technology ventures, including serving as President and CEO of SolarGrid Energy Inc., and currently leads Eminent-Tech Corporation, a consulting firm specializing in data science, advanced analytics, and operational excellence.
Dr. Rauf has authored over 50 international scientific publications and holds patents in advanced materials and process engineering. His research spans predictive analytics, machine learning, nanotechnology, and thin-film materials, with applications in renewable energy, nano-biosensors, and point-of-care diagnostics. More recently, his work has focused on integrating artificial intelligence with physical systems, including the development of physics-informed models and data-driven scientific discovery frameworks.
In addition to his industrial and research contributions, Dr. Rauf has maintained a strong commitment to education. He serves as an Adjunct Professor in the Department of Physics and Astronomy at York University and has been affiliated with Toronto Metropolitan University (formerly Ryerson University) as a research associate. He has also held international academic appointments, including Visiting Professor at the University of Energy and Natural Resources in Ghana and Foreign Faculty at COMSATS Institute of Information Technology in Pakistan.
Dr. Rauf has contributed extensively to professional and public service. He has served as an appointed Council Member of the Ontario College of Teachers, Vice-Chair of the Board of Directors at Blue Hills Child and Family Services, and as a panel expert for the International Renewable Energy Agency (IRENA). Through these roles, he has supported policy development, governance, and the advancement of education and renewable energy initiatives at national and international levels.
Across academia, industry, and entrepreneurship, Dr. Rauf’s work is unified by a central theme: the application of rigorous scientific principles, statistical thinking, and machine learning to solve complex, real-world problems. His current focus lies at the intersection of physics and artificial intelligence, where he is actively contributing to the emerging field of scientific machine learning.
Descriere
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.