Data-Driven Intelligent Modeling and Optimization Algorithms for Industrial Processes (Hardcover)

Zixin Huang

ISBN 10: 3725849110 / ISBN 13: 9783725849116

Published by Mdpi AG, 2025

About this Item

Hardcover. The aim of this Special Issue is to explore the multifaceted aspects of data-driven intelligent modeling and optimization algorithms for industrial processes. The main goals are to harness the power of data to improve control, decision making, and parameter optimization, and to drive industrial systems to unprecedented levels of efficiency, reliability, and adaptability. Research areas in this Special Issue include digital twin technology, multimodal data recognition, sensor data ingestion and real-time processing, multi-objective path-planning, conditional generative adversarial network, generating job recommendations, comprehensive risk assessment, large language models, self-supervised key-point learning, trustworthy article ranking, engine optimization model, and bioinspired generative design. These powerful and intelligent algorithms use data for control, decision making, and parameter optimization, driving industrial systems to unprecedented levels of efficiency, reliability, and adaptability. By sharing their practice and insights in the development and application of these new technologies, the authors of the articles in this reprint have demonstrated the value of data-driven intelligent modeling and optimization algorithms for industrial processes, providing readers with valuable ideological inspiration in the field. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Seller Inventory # 9783725849116

Bibliographic Details

• Title: Data-Driven Intelligent Modeling and Optimization Algorithms for Industrial Processes (Hardcover)
• Publisher: Mdpi AG
• Publication Date: 2025
• Language: English
• ISBN 10: 3725849110
• ISBN 13: 9783725849116
• Binding: Hardcover
• Condition: new

About this title

Synopsis

The aim of this Special Issue is to explore the multifaceted aspects of data-driven intelligent modeling and optimization algorithms for industrial processes. The main goals are to harness the power of data to improve control, decision making, and parameter optimization, and to drive industrial systems to unprecedented levels of efficiency, reliability, and adaptability. Research areas in this Special Issue include digital twin technology, multimodal data recognition, sensor data ingestion and real-time processing, multi-objective path-planning, conditional generative adversarial network, generating job recommendations, comprehensive risk assessment, large language models, self-supervised key-point learning, trustworthy article ranking, engine optimization model, and bioinspired generative design. These powerful and intelligent algorithms use data for control, decision making, and parameter optimization, driving industrial systems to unprecedented levels of efficiency, reliability, and adaptability. By sharing their practice and insights in the development and application of these new technologies, the authors of the articles in this reprint have demonstrated the value of data-driven intelligent modeling and optimization algorithms for industrial processes, providing readers with valuable ideological inspiration in the field.