High-performance computing and artificial intelligence in process engineering /

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"Version: 20250401"--Title page verso.Includes bibliographical references.1. Artificial intelligence and the future of process engineering / Nariman Piroozan -- 2. Machine learning in optimal control and process modeling / Yujia Wang and Zhe Wu -- 3. Graph-based control invariant set approximation and its applications / Song Bo, Benjamin Decardi-Nelson and Jinfeng Liu -- 4. Machine learning-based multiscale modeling and control of quantum dot manufacturing and their applications / Niranjan Sitapure, Parth Shah and Joseph Sang-Il Kwon -- 5. The rise of time-travelers : are transformer-based models the key to unlocking a new paradigm in surrogate modeling for dynamic systems? / Joseph Sang-Il Kwon -- 6. Optimization-based algorithms for solving inverse problems of parabolic PDEs / Yi Heng, Chen Wang, Qingqing Yang and Junxuan Deng -- 7. Deep learning-based approach for solving forward and inverse partial differential equation problems / Yi Heng, Jianghang Gu, Guohong Xie and Jia Yi -- 8. An active subspace based swarm intelligence method with its application in optimal design problem / Jiu Luo, Ke Chen, Junzhi Chen, Yutong Lu and Yi Heng -- 9. Supercomputing and machine-learning-aided optimal design of high permeability seawater reverse osmosis membrane systems / Jiu Luo, Mingheng Li and Yi Heng -- 10. Supercomputing-based inverse identification of high-resolution atmospheric pollutant source intensity distributions / Mingming Huang and Yi Heng -- 11. Enhancing boiling heat transfer via model-based experimental analysis / Yi Heng, Min Hong and Dongchuan Mo.Full-text restricted to subscribers or individual document purchasers.High-performance computing (HPC) and artificial intelligence (AI) in process engineering involve complex system modelling, data analysis, optimization design, and real-time monitoring. Key methods include data integration, model construction, optimization algorithms, machine learning, deep learning, parallel computing, and real-time analytics. These techniques significantly enhance production efficiency, reduce costs, and improve system stability. They also promote industrial intelligence, creating new opportunities and challenges in process engineering. This integration supports the advancement of Industry 4.0 and smart manufacturing.Researchers and industrial practitioners in process engineering and manufacturing who are interested in artificial intelligence and high-performance computing.Also available in print.Mode of access: World Wide Web.System requirements: Adobe Acrobat Reader, EPUB reader, or Kindle reader.Mingheng Li is a professor of chemical engineering specializing in process systems engineering, with a focus on materials, energy, and environmental applications. He has pioneered innovations in the processing of low-emissivity and self-cleaning coatings and advanced non-conventional dynamic and cyclic reverse osmosis techniques. He has served as an editor for the American Institute of Physics Publishing. Yi Heng obtained his PhD degree from RWTH Aachen University, Germany. He is a professor of applied mathematics. His work focuses on inverse problems, high performance computing, artificial intelligence, and their applications to various areas of science and engineering. He has served as an executive member of the editorial board for Science Bulletin.Title from PDF title page (viewed on May 1, 2025).

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Publisher

: .,

Collation

1 online resource (various pagings) :illustrations (some color).

Language

English

ISBN/ISSN

9780750361743

Classification

670.285

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-

Edition

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Subject(s)

Artificial intelligence
High performance computing.
Production engineering
Production engineering.
TECHNOLOGY & ENGINEERING / Manufacturing.

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Statement of Responsibility

edited by Mingheng Li, Yi Heng.

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