From Schr Dinger S Equation To Deep Learning A Quantum Approach
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From Schr Dinger S Equation To Deep Learning A Quantum Approach
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Author : N.B. Singh
language : en
Publisher: N.B. Singh
Release Date :
From Schr Dinger S Equation To Deep Learning A Quantum Approach written by N.B. Singh and has been published by N.B. Singh this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.
"From Schrödinger's Equation to Deep Learning: A Quantum Approach" offers a captivating exploration that bridges the realms of quantum mechanics and deep learning. Tailored for scientists, researchers, and enthusiasts in both quantum physics and artificial intelligence, this book delves into the symbiotic relationship between quantum principles and cutting-edge deep learning techniques. Covering topics such as quantum-inspired algorithms, neural networks, and computational advancements, the book provides a comprehensive overview of how quantum approaches enrich and influence the field of deep learning. With clarity and depth, it serves as an enlightening resource for those intrigued by the dynamic synergy between quantum mechanics and the transformative potential of deep learning.
Fundamentals Schr Dinger S Equation To Deep Learning
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Author : N.B. Singh
language : en
Publisher: N.B. Singh
Release Date :
Fundamentals Schr Dinger S Equation To Deep Learning written by N.B. Singh and has been published by N.B. Singh this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.
"Focusing on the journey from understanding Schrödinger's Equation to exploring the depths of Deep Learning, this book serves as a comprehensive guide for absolute beginners with no mathematical backgrounds. Starting with fundamental concepts in quantum mechanics, the book gradually introduces readers to the intricacies of Schrödinger's Equation and its applications in various fields. With clear explanations and accessible language, readers will delve into the principles of quantum mechanics and learn how they intersect with modern technologies such as Deep Learning. By bridging the gap between theoretical physics and practical applications, this book equips readers with the knowledge and skills to navigate the fascinating world of quantum mechanics and embark on the exciting journey of Deep Learning."
Machine Learning Meets Quantum Physics
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Author : Kristof T. Schütt
language : en
Publisher: Springer Nature
Release Date : 2020-06-03
Machine Learning Meets Quantum Physics written by Kristof T. Schütt and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-03 with Science categories.
Designing molecules and materials with desired properties is an important prerequisite for advancing technology in our modern societies. This requires both the ability to calculate accurate microscopic properties, such as energies, forces and electrostatic multipoles of specific configurations, as well as efficient sampling of potential energy surfaces to obtain corresponding macroscopic properties. Tools that can provide this are accurate first-principles calculations rooted in quantum mechanics, and statistical mechanics, respectively. Unfortunately, they come at a high computational cost that prohibits calculations for large systems and long time-scales, thus presenting a severe bottleneck both for searching the vast chemical compound space and the stupendously many dynamical configurations that a molecule can assume. To overcome this challenge, recently there have been increased efforts to accelerate quantum simulations with machine learning (ML). This emerging interdisciplinary community encompasses chemists, material scientists, physicists, mathematicians and computer scientists, joining forces to contribute to the exciting hot topic of progressing machine learning and AI for molecules and materials. The book that has emerged from a series of workshops provides a snapshot of this rapidly developing field. It contains tutorial material explaining the relevant foundations needed in chemistry, physics as well as machine learning to give an easy starting point for interested readers. In addition, a number of research papers defining the current state-of-the-art are included. The book has five parts (Fundamentals, Incorporating Prior Knowledge, Deep Learning of Atomistic Representations, Atomistic Simulations and Discovery and Design), each prefaced by editorial commentary that puts the respective parts into a broader scientific context.
Emergence In Context
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Author : Robert C. Bishop
language : en
Publisher: Oxford University Press
Release Date : 2022
Emergence In Context written by Robert C. Bishop and has been published by Oxford University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with Philosophy categories.
Science, philosophy of science, and metaphysics have long been concerned with the question of how order, stability, and novelty are possible and how they happen. How can order come out of disorder? This book introduces a new account, contextual emergence, seeking to answer these questions. The authors offer an alternative picture of the world with an alternative account of how novelty and order arise, and how both are possible. Contextual emergence is grounded primarily in the sciences as opposed to logic or metaphysics. It is both an explanatory and ontological account of emergence that gets beyond the impasse between "weak" and "strong" emergence in the emergence debates. It challenges the "foundationalist" or hierarchical picture of reality and emphasizes the ontological and explanatory fundamentality of multiscale stability conditions and their contextual constraints, often operating globally over interconnected, interdependent, and interacting entities and their multiscale relations. It also focuses on the conditions that make the existence, stability, and persistence of emergent systems and their states and observables possible. These conditions and constraints are irreducibly multiscale relations, so it is not surprising that scientific explanation is often multiscale. Such multiscale conditions act as gatekeepers for systems to access modal possibilities (e.g., reducing or enhancing a system's degrees of freedom). Using examples from across the sciences, ranging from physics to biology to neuroscience and beyond, this book demonstrates that there is an empirically well-grounded, viable alternative to ontological reductionism coupled with explanatory anti-reductionism (weak emergence) and ontological disunity coupled with the impossibility of robust scientific explanation (strong emergence). Central metaphysics of science concerns are also addressed. Emergence in Context: A Treatise in Twenty-First Century Natural Philosophy is written primarily for philosophers of science, but also professional scientists from multiple disciplines who are interested in emergence and particularly in the metaphysics of science.
Artificial Neural Networks And Machine Learning Icann 2019 Workshop And Special Sessions
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Author : Igor V. Tetko
language : en
Publisher: Springer Nature
Release Date : 2019-09-10
Artificial Neural Networks And Machine Learning Icann 2019 Workshop And Special Sessions written by Igor V. Tetko and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-10 with Computers categories.
The proceedings set LNCS 11727, 11728, 11729, 11730, and 11731 constitute the proceedings of the 28th International Conference on Artificial Neural Networks, ICANN 2019, held in Munich, Germany, in September 2019. The total of 277 full papers and 43 short papers presented in these proceedings was carefully reviewed and selected from 494 submissions. They were organized in 5 volumes focusing on theoretical neural computation; deep learning; image processing; text and time series; and workshop and special sessions.
Deterministic Stochastic And Deep Learning Methods For Computational Electromagnetics
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Author : Wei Cai
language : en
Publisher: Springer Nature
Release Date : 2025-03-02
Deterministic Stochastic And Deep Learning Methods For Computational Electromagnetics written by Wei Cai and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-02 with Mathematics categories.
This book provides a well-balanced and comprehensive picture based on clear physics, solid mathematical formulation, and state-of-the-art useful numerical methods in deterministic, stochastic, deep neural network machine learning approaches for computer simulations of electromagnetic and transport processes in biology, microwave and optical wave devices, and nano-electronics. Computational research has become strongly influenced by interactions from many different areas including biology, physics, chemistry, engineering, etc. A multifaceted approach addressing the interconnection among mathematical algorithms and physical foundation and application is much needed to prepare graduate students and researchers in applied mathematics and sciences and engineering for innovative advanced computational research in many applications areas, such as biomolecular solvation in solvents, radar wave scattering, the interaction of lights with plasmonic materials, plasma physics, quantum dots, electronic structure, current flows in nano-electronics, and microchip designs, etc.
Computational Analysis And Deep Learning For Medical Care
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Author : Amit Kumar Tyagi
language : en
Publisher: John Wiley & Sons
Release Date : 2021-08-24
Computational Analysis And Deep Learning For Medical Care written by Amit Kumar Tyagi and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-08-24 with Computers categories.
The book details deep learning models like ANN, RNN, LSTM, in many industrial sectors such as transportation, healthcare, military, agriculture, with valid and effective results, which will help researchers find solutions to their deep learning research problems. We have entered the era of smart world devices, where robots or machines are being used in most applications to solve real-world problems. These smart machines/devices reduce the burden on doctors, which in turn make their lives easier and the lives of their patients better, thereby increasing patient longevity, which is the ultimate goal of computer vision. Therefore, the goal in writing this book is to attempt to provide complete information on reliable deep learning models required for e-healthcare applications. Ways in which deep learning can enhance healthcare images or text data for making useful decisions are discussed. Also presented are reliable deep learning models, such as neural networks, convolutional neural networks, backpropagation, and recurrent neural networks, which are increasingly being used in medical image processing, including for colorization of black and white X-ray images, automatic machine translation images, object classification in photographs/images (CT scans), character or useful generation (ECG), image caption generation, etc. Hence, reliable deep learning methods for the perception or production of better results are a necessity for highly effective e-healthcare applications. Currently, the most difficult data-related problem that needs to be solved concerns the rapid increase of data occurring each day via billions of smart devices. To address the growing amount of data in healthcare applications, challenges such as not having standard tools, efficient algorithms, and a sufficient number of skilled data scientists need to be overcome. Hence, there is growing interest in investigating deep learning models and their use in e-healthcare applications. Audience Researchers in artificial intelligence, big data, computer science, and electronic engineering, as well as industry engineers in transportation, healthcare, biomedicine, military, agriculture.
Computational Physics
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Author : Devang Patil
language : en
Publisher: Educohack Press
Release Date : 2025-02-20
Computational Physics written by Devang Patil and has been published by Educohack Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-20 with Science categories.
"Computational Physics: Basic Concepts" serves as an indispensable guide for students, researchers, and enthusiasts exploring the intersection of physics and computational methods. This book offers a comprehensive exploration of the fundamental principles of computational physics, providing a solid foundation to tackle complex problems in various branches of physics. The book begins by elucidating the foundational principles and theoretical underpinnings essential for effective computational simulations. It covers a variety of numerical techniques, including finite difference methods and Monte Carlo simulations, with practical examples and applications. Recognizing the importance of coding skills, it includes a section on programming tailored for physicists, teaching readers to implement numerical algorithms using popular programming languages. "Computational Physics: Basic Concepts" extends its coverage to diverse branches of physics such as classical mechanics, electromagnetism, quantum mechanics, and statistical physics, illustrating the versatility of computational techniques. Each chapter includes problem-solving exercises designed to reinforce understanding and enhance computational skills. Techniques for data visualization and interpretation are discussed, enabling effective communication of findings. The book also shares practical tips and best practices to optimize computational workflows and avoid common pitfalls. Whether you're a student new to computational physics or a seasoned researcher, "Computational Physics: Basic Concepts" provides a thorough and accessible resource for mastering the essential elements of this dynamic field.
Robotic Mechanical Systems Fundamentals
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Author : Shridhar Shastri
language : en
Publisher: Educohack Press
Release Date : 2025-02-20
Robotic Mechanical Systems Fundamentals written by Shridhar Shastri and has been published by Educohack Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-20 with Technology & Engineering categories.
"Robotic Mechanical Systems Fundamentals" serves as a comprehensive guide to understanding the core principles and technological intricacies of robotic systems in today’s rapidly evolving landscape. We offer an in-depth exploration of the mechanical foundations that drive the design, control, and functionality of robots, making it an essential resource for students, researchers, and industry professionals. Our journey begins with a thorough examination of the fundamental concepts and historical developments that shape robotics. Readers will gain insights into the dynamics of robotic systems through the Newton-Euler equations, paving the way for a deeper understanding of the Lagrange formulation, which offers a powerful framework for analyzing robot motion. Focusing on dynamic modeling, we provide a detailed look at the mechanisms governing the behavior of manipulators, emphasizing the complexities involved in designing and controlling robotic arms. Additionally, we address control forces and torques, highlighting strategies to ensure precision and efficiency in robotic actions. With a holistic approach that considers the ethical and societal implications of robotics, "Robotic Mechanical Systems Fundamentals" balances theoretical foundations with practical applications, making it accessible for beginners and valuable for seasoned professionals. Authored by experts, our book equips readers to navigate the fascinating world of robotics, inspiring a deeper appreciation for the technologies that shape our future.
Machine Learning Based Modelling In Atomic Layer Deposition Processes
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Author : Oluwatobi Adeleke
language : en
Publisher: CRC Press
Release Date : 2023-12-15
Machine Learning Based Modelling In Atomic Layer Deposition Processes written by Oluwatobi Adeleke and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-12-15 with Technology & Engineering categories.
While thin film technology has benefited greatly from artificial intelligence (AI) and machine learning (ML) techniques, there is still much to be learned from a full-scale exploration of these technologies in atomic layer deposition (ALD). This book provides in-depth information regarding the application of ML-based modeling techniques in thin film technology as a standalone approach and integrated with the classical simulation and modeling methods. It is the first of its kind to present detailed information regarding approaches in ML-based modeling, optimization, and prediction of the behaviors and characteristics of ALD for improved process quality control and discovery of new materials. As such, this book fills significant knowledge gaps in the existing resources as it provides extensive information on ML and its applications in film thin technology. Offers an in-depth overview of the fundamentals of thin film technology, state-of-the-art computational simulation approaches in ALD, ML techniques, algorithms, applications, and challenges. Establishes the need for and significance of ML applications in ALD while introducing integration approaches for ML techniques with computation simulation approaches. Explores the application of key techniques in ML, such as predictive analysis, classification techniques, feature engineering, image processing capability, and microstructural analysis of deep learning algorithms and generative model benefits in ALD. Helps readers gain a holistic understanding of the exciting applications of ML-based solutions to ALD problems and apply them to real-world issues. Aimed at materials scientists and engineers, this book fills significant knowledge gaps in existing resources as it provides extensive information on ML and its applications in film thin technology. It also opens space for future intensive research and intriguing opportunities for ML-enhanced ALD processes, which scale from academic to industrial applications.