Artificial Intelligence (AI) is defined as the simulation of human intelligence through the mimicking of the human brain for analysis, modeling, and decision‐making. Science and engineering problem solving requires modeling of physical phenomena, and humans approach the solution of scientific and engineering problems differently from other problems. Artificial Intelligence for Science and Engineering Applications addresses the unique differences in how AI should be developed and used in science and engineering. Through the inclusion of definitions and detailed examples, this book describes the actual and realistic requirements as well as what characteristics must be avoided for correct and successful science and engineering applications of AI. This book: Offers a brief history of AI and covers science and engineering applications Explores the modeling of physical phenomena using AI Discusses explainable AI (XAI) applications Covers the ethics of AI in science and engineering Features real‐world case studies Offering a probing view into the unique nature of scientific and engineering exploration, this book will be of interest to generalists and experts looking to expand their understanding of how AI can better tackle and advance technology and developments in scientific and engineering disciplines.

New engineering materials, techniques and applications are constantly being researched and developed, and keeping up to speed with the latest advances is crucial for engineers if they are to successfully address the challenges they face in their work. This book presents the selected proceedings of MMSE2023, the 9th International Conference on Advances in Machinery, Materials Science and Engineering Applications, jointly organized by the SAE-Supmeca, France and China University of Geosciences (Wuhan) and held on 22 and 23 July 2023 in Wuhan, China. For the past 12 years, this annual conference has collated recent advances and experiences, identified emerging trends and provided a platform for participants from academia and industry to exchange information and views, helping to address the world’s machinery and engineering challenges. The book contains 4 sections: mechanical engineering, material science and manufacturing technology; electrical engineering, automation and control; modeling, simulation and optimization techniques in engineering; and advanced engineering technologies and applications. A total of 241 submissions were received for MMSE2023, of which 151 papers were selected for the conference and for publication by means of a rigorous international peer-review process. These papers present exciting ideas and methods that will open novel research directions for different communities. Offering a current overview of the latest research and applications in machinery and materials-science engineering, the book will be of interest to all those working in the field.

This book is a quick review of machine learning methods for engineeringapplications. It provides an introduction to the principles of machine learningand common algorithms in the first section. Proceeding chapters summarize andanalyze the existing scholarly work and discuss some general issues in this field.Next, it offers some guidelines on applying machine learning methods to softwareengineering tasks. Finally, it gives an outlook into some of the futuredevelopments and possibly new research areas of machine learning and artificialintelligence in general.Techniques highlighted in the book include: Bayesian models, supportvector machines, decision tree induction, regression analysis, and recurrent andconvolutional neural network. Finally, it also intends to be a reference book. Key Features:Describes real-world problems that can be solved using machine learningExplains methods for directly applying machine learning techniques to concrete real-world problemsExplains concepts used in Industry 4.0 platforms, including the use and integration of AI, ML, Big Data, NLP, and the Internet of Things (IoT). It does not require prior knowledge of the machine learning This book is meantto be an introduction to artificial intelligence (AI), machine earning, and itsapplications in Industry 4.0. It explains the basic mathematical principlesbut is intended to be understandable for readers who do not have a backgroundin advanced mathematics.

The purpose of this book is to present 10 scientific and engineering works whose numerical and graphical analysis were all constructed using the power of MATLAB® tools. The first five chapters of this book show applications in seismology, meteorology and natural environment. Chapters 6 and 7 focus on modeling and simulation of Water Distribution Networks. Simulation was also applied to study wide area protection for interconnected power grids (Chapter 8) and performance of conical antennas (Chapter 9). The last chapter deals with depth positioning of underwater robot vehicles. Therefore, this book is a collection of interesting examples of where this computational package can be applied.

This book constitutes the revised selected papers of the 21st Smoky Mountains Computational Sciences and Engineering Conference, SMC 2021, held in Oak Ridge, TN, USA*, in October 2021. The 33 full papers and 3 short papers presented were carefully reviewed and selected from a total of 88 submissions. The papers are organized in topical sections of computational applications: converged HPC and artificial intelligence; advanced computing applications: use cases that combine multiple aspects of data and modeling; advanced computing systems and software: connecting instruments from edge to supercomputers; deploying advanced computing platforms: on the road to a converged ecosystem; scientific data challenges. *The conference was held virtually due to the COVID-19 pandemic.

Dr.Ravi Kumar Saidala, Associate Professor, Department of Computer Science and Engineering (Data Science), CMR University, Bangalore, Karnataka, India. Dr.D.Usha Rani, Associate Professor, Department of Computer Science and Applications, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India. Ms.Indu.B, Assistant Professor, Department of Computer Science Engineering, Dayananda Sagar Academy of Technology and Management (DSATM), Bangalore, Karnataka, India. Dr.Shanthala.P.T, Assistant Professor, Department of Computer Science Engineering, PES University, Bangalore, Karnataka, India.

The latest research innovations and enhanced technologies have altered the discipline of materials science and engineering. As a direct result of these developments, new trends in Materials Science and Engineering (MSE) pedagogy have emerged that require attention. The Handbook of Research on Recent Developments in Materials Science and Corrosion Engineering Education brings together innovative and current advances in the curriculum design and course content of MSE education programs. Focusing on the application of instructional strategies, pedagogical frameworks, and career preparation techniques, this book is an essential reference source for academicians, engineering practitioners, researchers, and industry professionals interested in emerging and future trends in MSE training and education.

The Power of Artificial Intelligence for the Next-Generation Oil and Gas Industry Comprehensive resource describing how operations, outputs, and offerings of the oil and gas industry can improve via advancements in AI The Power of Artificial Intelligence for the Next-Generation Oil and Gas Industry describes the proven and promising digital technologies and tools available to empower the oil and gas industry to be future-ready. It shows how the widely reported limitations of the oil and gas industry are being nullified through the application of breakthrough digital technologies and how the convergence of digital technologies helps create new possibilities and opportunities to take this industry to its next level. The text demonstrates how scores of proven digital technologies, especially in AI, are useful in elegantly fulfilling complicated requirements such as process optimization, automation and orchestration, real-time data analytics, productivity improvement, employee safety, predictive maintenance, yield prediction, and accurate asset management for the oil and gas industry. The text differentiates and delivers sophisticated use cases for the various stakeholders, providing easy-to-understand information to accurately utilize proven technologies towards achieving real and sustainable industry transformation. The Power of Artificial Intelligence for the Next-Generation Oil and Gas Industry includes information on: How various machine and deep learning (ML/DL) algorithms, the prime modules of AI, empower AI systems to deliver on their promises and potential Key use cases of computer vision (CV) and natural language processing (NLP) as they relate to the oil and gas industry Smart leverage of AI, the Industrial Internet of Things (IIoT), cyber physical systems, and 5G communication Event-driven architecture (EDA), microservices architecture (MSA), blockchain for data and device security, and digital twins Clearly expounding how the power of AI and other allied technologies can be meticulously leveraged by the oil and gas industry, The Power of Artificial Intelligence for the Next-Generation Oil and Gas Industry is an essential resource for students, scholars, IT professionals, and business leaders in many different intersecting fields.