Applied Artificial Intelligence for Engineering Applications: Empowering Transformative Innovations in Multidisciplinary

Name: Applied Artificial Intelligence for Engineering Applications: Empowering Transformative Innovations in Multidisciplinary
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Bol Applied Artificial Intelligence for Engineering Applications: Empowering Transformative Innovations in Multidisciplinary Engineering offers an integrated view of AI’s transformative potential in multidisciplinary engineering, bridging the gap between AI advances and their applications in various engineering disciplines, including civil, mechanical, electrical, chemical, and robotics. The book starts with an introduction to the field, providing an overview of AI technologies and their relevance to engineering, discussing different AI approaches such as machine learning, reinforcement learning, neural networks, and natural language processing. In addition, the computational tools and software used for each of these approaches are covered, as are AI-driven innovations in electronics design and manufacturing and the role of AI in the development of advanced sensors and IoT devices. Chapters then cover the deployment of AI in specific engineering disciplines, outlining its use in civil engineering, mechanical engineering, production engineering, and chemical engineering. The use of AI as a solution for energy efficiency and management and renewable energy integration is also discussed.

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Applied Artificial Intelligence for Engineering Applications: Empowering Transformative Innovations in Multidisciplinary Engineering offers an integrated view of AI’s transformative potential in multidisciplinary engineering, bridging the gap between AI advances and their applications in various engineering disciplines, including civil, mechanical, electrical, chemical, and robotics. The book starts with an introduction to the field, providing an overview of AI technologies and their relevance to engineering, discussing different AI approaches such as machine learning, reinforcement learning, neural networks, and natural language processing. In addition, the computational tools and software used for each of these approaches are covered, as are AI-driven innovations in electronics design and manufacturing and the role of AI in the development of advanced sensors and IoT devices. Chapters then cover the deployment of AI in specific engineering disciplines, outlining its use in civil engineering, mechanical engineering, production engineering, and chemical engineering. The use of AI as a solution for energy efficiency and management and renewable energy integration is also discussed.

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