Deep Learning for Physics Research using Python: A Comprehensive Guide to Modern AI Techniques in Scientific DiscoveryКНИГИ » ПРОГРАММИНГ
Название: Deep Learning for Physics Research using Python: A Comprehensive Guide to Modern AI Techniques in Scientific Discovery Автор: Aarav Joshi Издательство: 101 Books Год: 2025 Страниц: 1597 Язык: английский Формат: epub (true) Размер: 12.3 MB
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 (AI) 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.
The mathematical foundation for Deep Learning in physics research is both rich and essential. This section explores the key mathematical concepts that bridge traditional physics with modern Deep Learning techniques. From linear algebra to differential equations, these mathematical tools enable researchers to develop sophisticated models that can discover patterns in complex physical systems, predict behaviors, and even suggest new theoretical frameworks. By understanding these mathematical prerequisites, physicists can leverage deep learning not just as a computational tool, but as a framework for advancing scientific discovery. The intersection of mathematics, physics, and machine learning creates a powerful approach to solving previously intractable problems across various domains of physical science. Linear algebra forms the backbone of Deep Learning mathematics. At its core, Deep Learning manipulates tensors – multi-dimensional arrays of numbers – which are generalizations of matrices and vectors. When we represent physical data or model parameters, we typically use vectors (one-dimensional arrays) or matrices (two-dimensional arrays).
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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Deep Learning for Physics Research using Python: A Comprehensive Guide to Modern AI Techniques in Scientific Discovery 
