Deep Learning for Physics Research using Python: A Comprehensive Guide to Modern AI Techniques in Scientific Discovery (eBook, ePUB)
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Deep Learning for Physics Research using Python: A Comprehensive Guide to Modern AI Techniques in Scientific Discovery is the definitive resource for physicists, researchers, and students seeking to harness the transformative power of artificial intelligence in scientific research. This comprehensive guide bridges the gap between cutting-edge deep learning methodologies and practical physics applications, providing hands-on implementation using Python and modern frameworks like TensorFlow, PyTorch, and Keras.The book systematically covers 14 essential chapters, from foundational neural network...
Deep Learning for Physics Research using Python: A Comprehensive Guide to Modern AI Techniques in Scientific Discovery is the definitive resource for physicists, researchers, and students seeking to harness the transformative power of artificial intelligence in scientific research. This comprehensive guide bridges the gap between cutting-edge deep learning methodologies and practical physics applications, providing hands-on implementation using Python and modern frameworks like TensorFlow, PyTorch, and Keras.
The book systematically covers 14 essential chapters, from foundational neural network concepts to advanced architectures including Physics-Informed Neural Networks (PINNs), Graph Neural Networks, and Transformer models. Readers will master convolutional networks for detector data analysis, recurrent networks for time series physics, generative models for synthetic data creation, and uncertainty quantification techniques crucial for scientific validity.
Each chapter features detailed Python implementations, real-world case studies from particle physics to climate modeling, and practical exercises with downloadable code and datasets. The text emphasizes physics-specific considerations including conservation laws, symmetry preservation, and experimental uncertainty handling. Advanced topics include automated experiment design, quantum-classical hybrid networks, and ethical AI deployment in research environments.
Whether you're analyzing cosmic ray data, predicting material properties, or developing digital twins for complex systems, this book provides the essential knowledge and practical tools to revolutionize your physics research through modern AI techniques.
The book systematically covers 14 essential chapters, from foundational neural network concepts to advanced architectures including Physics-Informed Neural Networks (PINNs), Graph Neural Networks, and Transformer models. Readers will master convolutional networks for detector data analysis, recurrent networks for time series physics, generative models for synthetic data creation, and uncertainty quantification techniques crucial for scientific validity.
Each chapter features detailed Python implementations, real-world case studies from particle physics to climate modeling, and practical exercises with downloadable code and datasets. The text emphasizes physics-specific considerations including conservation laws, symmetry preservation, and experimental uncertainty handling. Advanced topics include automated experiment design, quantum-classical hybrid networks, and ethical AI deployment in research environments.
Whether you're analyzing cosmic ray data, predicting material properties, or developing digital twins for complex systems, this book provides the essential knowledge and practical tools to revolutionize your physics research through modern AI techniques.
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