Artificial Intelligence Techniques in Mathematical Modeling and Optimization
暫譯: 數學建模與優化中的人工智慧技術

Awasthi, Mukesh Kumar, Kumar, Sanoj, Saini, Deepika

Artificial Intelligence Techniques in Mathematical Modeling and Optimization
  • 出版商: CRC
  • 出版日期: 2026-04-30
  • 售價: $7,660
  • 貴賓價: 9.5 折 $7,277
  • 語言: 英文
  • 頁數: 328
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 1041060033
  • ISBN-13: 9781041060031
  • 相關分類: Machine Learning

    • 尚未上市，無法訂購

商品描述

Artificial Intelligence Techniques in Mathematical Modeling and Optimization offers a dynamic and comprehensive examination of the intersection between artificial intelligence and mathematical modeling. This edited volume brings together innovative research exploring how AI-driven methods revolutionize traditional approaches to complex optimization problems, enabling enhanced performance, interpretability, and real-world applicability across diverse domains.

Covering foundational and advanced topics, the book introduces readers to machine learning, deep learning, and reinforcement learning as critical tools for modeling high-dimensional, nonlinear, and stochastic systems. Chapters delve into essential aspects like data pre-processing, feature engineering, neural network architectures, swarm intelligence, quantum optimization, and multi-objective decision-making. Emerging techniques such as Fire Hawk Optimization Plus (FHO+), hybrid deep learning-quantum frameworks, and explainable AI (XAI) are discussed in the context of real-world scenarios ranging from energy systems and manufacturing to disaster prediction and healthcare analytics.

This volume uniquely bridges theory and application by integrating algorithmic strategies with case studies on predictive maintenance, renewable energy optimization, cyclone detection, heart disease prediction, and postpartum mental health risk assessment. It also investigates the role of circular economy principles in inventory optimization and examines future trends including neuromorphic computing and ethical AI.

Key Features:

- Systematic exploration of AI-based optimization in mathematical modeling.

- In-depth coverage of ML/DL methods, quantum algorithms, and nature-inspired techniques.

- Practical applications in industrial manufacturing, healthcare, smart energy, and environmental resilience.

- Detailed discussions on model training, generalization, hyperparameter tuning, and overfitting control.

- Includes practical tools such as AutoML, PINNs, CNNs, and quantum convolutional networks.

- Forward-looking insights into sustainable optimization, interpretability, and autonomous AI systems.

This volume is an essential resource for graduate students, researchers, and practitioners in applied mathematics, computer science, engineering, and data-driven optimization, offering the theoretical depth and application-driven clarity needed to tackle modern scientific and engineering challenges through AI-powered modeling and decision systems.

商品描述(中文翻譯)

《數學建模與優化中的人工智慧技術》提供了一個動態且全面的檢視，探討人工智慧與數學建模之間的交集。本書匯集了創新的研究，探索如何利用人工智慧驅動的方法徹底改變傳統的複雜優化問題解決方式，從而提升性能、可解釋性及在各個領域的實際應用。

本書涵蓋基礎與進階主題，向讀者介紹機器學習（machine learning）、深度學習（deep learning）和強化學習（reinforcement learning）作為建模高維度、非線性及隨機系統的關鍵工具。各章深入探討數據預處理、特徵工程、神經網絡架構、群體智慧、量子優化及多目標決策等重要方面。新興技術如火鷹優化加（Fire Hawk Optimization Plus, FHO+）、混合深度學習-量子框架及可解釋的人工智慧（explainable AI, XAI）在能源系統、製造業、災害預測及醫療分析等現實場景中進行討論。

本書獨特地將理論與應用相結合，通過整合算法策略與預測性維護、可再生能源優化、氣旋檢測、心臟病預測及產後心理健康風險評估的案例研究來實現。它還探討了循環經濟原則在庫存優化中的角色，並檢視未來趨勢，包括神經形態計算（neuromorphic computing）和倫理人工智慧（ethical AI）。

主要特色：
- 系統性探索基於人工智慧的數學建模優化。
- 深入涵蓋機器學習/深度學習方法、量子算法及自然啟發技術。
- 在工業製造、醫療保健、智慧能源及環境韌性中的實際應用。
- 詳細討論模型訓練、泛化、超參數調整及過擬合控制。
- 包含實用工具如AutoML、PINNs、CNNs及量子卷積網絡。
- 對可持續優化、可解釋性及自主人工智慧系統的前瞻性見解。

本書是應用數學、計算機科學、工程及數據驅動優化領域的研究生、研究人員及實務工作者的重要資源，提供了解決現代科學與工程挑戰所需的理論深度與應用驅動的清晰度，透過人工智慧驅動的建模與決策系統。

作者簡介

Mukesh Kumar Awasthi has done his Ph.D. on the topic "Viscous Correction for the Potential Flow Analysis of Capillary and Kelvin-Helmholtz instability". He is working as an Assistant Professor in the Department of Mathematics at Babasaheb Bhimrao Ambedkar University, Lucknow. Dr. Awasthi is specialized in the mathematical modeling of flow problems. He has taught courses of Fluid Mechanics, Discrete Mathematics, Partial differential equations, Abstract Algebra, Mathematical Methods, and Measure theory to postgraduate students. He has acquired excellent knowledge in the mathematical modeling of flow problems and he can solve these problems analytically as well as numerically. He has a good grasp of the subjects like viscous potential flow, electro-hydrodynamics, magneto-hydrodynamics, heat, and mass transfer. He has excellent communication skills and leadership qualities. He is self-motivated and responds to suggestions in a more convincing manner. Dr. Awasthi has qualified National Eligibility Test (NET) conducted on all India level in the year 2008 by the Council of Scientific and Industrial Research (CSIR) and got Junior Research Fellowship (JRF) and Senior Research Fellowship (SRF) for doing research. He has published 135 plus research publications (journal articles/books/book chapters/conference articles) in national and international journals and conferences. Also, he has published 19 books. He is also a series editor of Artificial Intelligence and Machine Learning for Intelligent Engineering Systems published by CRC Press. He has attended many symposia, workshops, and conferences in mathematics as well as fluid mechanics. He has got the "Research Awards" consecutively four times from 2013-2016 by the University of Petroleum and Energy Studies, Dehradun, India. He has also received the start-up research fund for his project "Nonlinear study of the interface in multilayer fluid system" from UGC, New Delhi. He is also listed among the top 2% of influential researchers in the world, as prepared by Stanford University based on Scopus data for the years 2022 and 2023. His Orcid is 0000-0002-6706-5226, Google Scholar web link is https: //scholar.google.co.in/citations?user=Dj3ktGAAAAAJ and research gate web link ishttps: //www.researchgate.net/profile/Mukesh-Awasthi-2.

Sanoj Kumar works as an assistant professor (SG) at Data Science Cluster, SOCS, UPES, Dehradun, Uttarakhand, India. Earlier, he worked as a postdoctoral fellow with the Department of Mathematics and Computer Science, University of Udine, Italy, from October 2013 to September 2014. He completed his PhD in mathematics from IIT Roorkee, India, in 2013. Dr. Kumar's research interests include image processing, computer vision, and machine learning. He has authored more than 25 papers published in referred international journals and conferences. He has also authored two book chapters. He is a reviewer for various journals such as ISA Transactions, IET Image Processing, Optical Engineering, Applied Mathematical Modeling, Mathematics, etc. He also got the best paper and young scientist awards in NETCRYPT 2020. His teaching area includes Engineering Mathematics I, Engineering Mathematics II, discrete mathematics, graph theory, optimization techniques, numerical analysis, linear algebra, probability and statistics, real analysis, complex analysis, differential equation, digital image processing and introduction to data science, etc.

Deepika Saini is an assistant professor at Graphic Era (deemed to be) University, Dehradun, Uttarakhand, India. Previously, in 2016, she received her Ph.D. in Mathematics from IIT Roorkee in India. She completed her M.Sc. in Mathematics from H.N.B. Garhwal University, Srinagar, Uttarakhand, India. She won the gold medal for securing first place among all PG students in her M.Sc. in 2005. Dr. Saini's research interests include computer vision, image processing, computer graphics, and their applications in various branches of engineering. She has published more than 20 papers in various international journals and reputed conferences. She has also authored a book chapter. She also got the best paper award in NETCRYPT 2020. Her teaching area includes Mathematics I, Mathematics II, Mathematics III, discrete mathematics, computer based numerical and statistics techniques, linear programming, numerical analysis, linear algebra, algebra, differential equation etc.

作者簡介(中文翻譯)

**Mukesh Kumar Awasthi** 於「毛細管及凱爾文-亥姆霍茲不穩定性之潛在流動分析的黏性修正」主題上取得博士學位。他目前在印度勒克瑙的巴巴薩赫布·比姆拉奧·安貝德卡大學數學系擔任助理教授。Awasthi 博士專注於流動問題的數學建模。他教授過流體力學、離散數學、偏微分方程、抽象代數、數學方法和測度理論等課程，對研究生進行教學。他在流動問題的數學建模方面擁有卓越的知識，能夠以解析和數值方式解決這些問題。他對黏性潛在流、電流動力學、磁流動力學、熱傳遞和質量傳遞等主題有良好的掌握。他具備優秀的溝通技巧和領導能力，並且自我激勵，對建議的回應更具說服力。Awasthi 博士於 2008 年通過印度科學與工業研究委員會（CSIR）舉辦的全印度國家資格考試（NET），並獲得了進行研究的初級研究獎學金（JRF）和高級研究獎學金（SRF）。他在國內外期刊和會議上發表了超過 **135 篇** 研究出版物（期刊文章/書籍/書籍章節/會議文章），並出版了 **19 本** 書籍。他還是由 CRC Press 出版的 **《人工智慧與機器學習在智能工程系統中的應用》** 系列的編輯。他參加了許多數學和流體力學的研討會、工作坊和會議。2013 至 2016 年期間，他連續四次獲得印度德拉敦石油與能源研究大學的「研究獎」。他還獲得了來自新德里的 UGC 的啟動研究基金，用於他的項目「多層流體系統界面的非線性研究」。他在斯坦福大學根據 Scopus 數據編制的 2022 和 2023 年全球 **前 2% 影響力研究人員** 名單中名列其中。他的 Orcid 是 **0000-0002-6706-5226**，Google Scholar 網頁鏈接是 **https://scholar.google.co.in/citations?user=Dj3ktGAAAAAJ**，ResearchGate 網頁鏈接是 **https://www.researchgate.net/profile/Mukesh-Awasthi-2**。

**Sanoj Kumar** 目前在印度烏塔拉坎德州德拉敦的 UPES 數據科學集群擔任助理教授（SG）。他曾於 2013 年 10 月至 2014 年 9 月在意大利烏迪內大學數學與計算機科學系擔任博士後研究員。他於 2013 年在印度 IIT Roorkee 獲得數學博士學位。Kumar 博士的研究興趣包括圖像處理、計算機視覺和機器學習。他已發表超過 25 篇在國際期刊和會議上經過審核的論文，並撰寫了兩個書籍章節。他是多個期刊的審稿人，如 ISA Transactions、IET Image Processing、Optical Engineering、Applied Mathematical Modeling、Mathematics 等。他在 NETCRYPT 2020 獲得最佳論文和青年科學家獎。他的教學領域包括工程數學 I、工程數學 II、離散數學、圖論、優化技術、數值分析、線性代數、概率與統計、實分析、複分析、微分方程、數位圖像處理和數據科學導論等。

**Deepika Saini** 是印度烏塔拉坎德州德拉敦的 Graphic Era（被認可為）大學的助理教授。她於 2016 年在印度 IIT Roorkee 獲得數學博士學位，並在 H.N.B. Garhwal University 獲得數學碩士學位。她在 2005 年的碩士學位中獲得金牌，成為所有研究生中的第一名。Saini 博士的研究興趣包括計算機視覺、圖像處理、計算機圖形學及其在各工程領域的應用。她在各種國際期刊和知名會議上發表了超過 20 篇論文，並撰寫了一個書籍章節。她在 NETCRYPT 2020 獲得最佳論文獎。她的教學領域包括數學 I、數學 II、數學 III、離散數學、基於計算機的數值與統計技術、線性規劃、數值分析、線性代數、代數、微分方程等。

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