Probability and Optimization for Engineers

Probabilistic Design, Optimum Design, Finite Element Method, Artificial Intelligence
1. Auflage 2026
ISBN: 978-3-11-914354-7
Verlag: De Gruyter

Buch, Englisch, 216 Seiten, Format (B × H): 170 mm x 240 mm, Gewicht: 387 g

Reihe: De Gruyter Textbook

covers the fundamentals of probabilistic design and optimum design and methods for high performance design. It presents the principle of finite element method in probabilistic and optimum design with solved specific interactive problems of finite element analysis using ANSYS. It explains artificial intelligence for optimum design using AI algorithms including machine learning, deep learning, artificial neural networks, Bayesian optimum design, Bayesian machine learning optimization, and genetic algorithms. 

• Probabilistic and optimum design course with easy, visual, and applied methods.
• Solved examples, assignments, problems and tutorials.
• Educational resource for engineering students and for practicing engineers.

Autoren/Hrsg.

Altabey, Wael A.

Fachgebiete

• Technische Wissenschaften Technik Allgemein Mathematik für Ingenieure
• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Angewandte Mathematik, Mathematische Modelle
• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Optimierung
• Mathematik | Informatik Mathematik Stochastik Wahrscheinlichkeitsrechnung

Über Autor(innen)

Dr. Wael A. Altabey is a full professor at department of Mechanical Engineering, Alexandria University, Alexandria, Egypt. Before that he was a research associate professor between 2018 to 2024 at International Institute for Urban Systems Engineering (IIUSE), Southeast University, Nanjing, China, and National and Local Joint Engineering Research Center for Basalt Fiber Production and Application Technology, Southeast University, Nanjing, Jiangsu, China, after completing a postdoctoral research fellowship for two years (2016-2018).

Since 2016 his researches have focused on the utilization of Artificial Intelligence (AI) based schemes for structural health monitoring (SHM) and Non-Destructive Testing (NDT) for damage classification, detection, diagnosis, prediction, dynamic response analysis, and Reliability evaluation in composite, and steel Structures (such as aircraft, wind turbines, pipes, bridges and industrial machines) at National and Local Joint Engineering Research Center for Basalt Fiber Production and Application Technology, Southeast University, Nanjing, Jiangsu, China. This is the only national R&D platform awarded by the National Development and Reform Commission in this industry with more than 30 national authorized patents. The center's international and national awards indicators have reached international and local leading levels and filling many technical gaps in China.

He participated in several research activities, which achieved from NSFC and private sectors. He listed in Stanford List of World's Top 2% Scientists from 2020, until now. He serves on various technical committees in several international conferences and workshops, guest editor of special Issues in several international scientific journals and on the editorial board of several international scientific journals in the field of artificial intelligence, mechanical, materials, and civil engineering. He is a peer reviewer of more than 160 international scientific journals. He is an author and co-author of more than 130 high impact journal papers, 60 scientific conference papers and 60 chapters, 10 academic and research books, patent, and delivered over 60 invited talks.

His research interests: Smart and Nanomaterials; Composite Structures; Structural Health Monitoring (SHM); Artificial Intelligence (AI); Non-Destructive Testing (NDT); Digital Twins Model of Structural Behavior, System Identification; Damage Detection: Vibration-Based Techniques; Fiber Optical Sensing Technique, Structural Control; Structural Resilience and Reliability, Hysteretic Systems, Micro/Nano Electro Mechanical Systems (MEMS/ NEMS), and Energy Harvesting Model for Self-Powered Sensors.