An Integrated Approach to Modeling and Optimization in Engineering and Science

An Integrated Approach to Modeling and Optimization in Engineering and Science is a technical book written with the aim to evaluate the modeling and design processes of engineering systems with an integrated approach.

Melih Savran earned a BS degree in mechanical engineering at Manisa Celal Bayar University in 2013. He earned MS and PhD degrees in mechanical engineering at -zmir Katip Çelebi University in 2017 and 2023, respectively. He continues to work as a researcher at the same university. His research areas include mechanics of solids, design and mathematical modeling, machine learning, stochastic optimization, and hybrid natural/synthetic composites. He has international publications on stochastic optimization and modeling in engineering, including book chapters, journal articles, and conference papers. Levent Aydin is an Associate Professor of Mechanical Engineering at -zmir Katip Çelebi University. He earned a PhD degree in mechanical engineering at -zmir Institute of Technology in 2011. His main research interests are stochastic optimization, mechanics of solids, biocomposites, biosensors, advanced engineering mathematics, hybrid neuro regression, and artificial intelligence modeling. Dr. Aydin has written more than 100 international publications on stochastic optimization and modeling in engineering, including book chapters, journal articles, and conference papers. He is also a consultant for many industrial research and development projects of international engineering firms. Dr. Aydin is the founder of the Optimization, Modeling and Applied Math Research Group (OMA-RG). He is the editor or author of Designing Engineering Structures Using Stochastic Optimization Methods, Bioelectrochemical Interface Engineering, Hybrid Natural Fiber Composites, Vegetable Fiber Composites and Their Technological Applications, and Fiber Technology for Fiber-Reinforced Composites.

1. Introduction. 2. Design of Experiment, Mathematical Modeling, and Optimization. 3. Comparison of ANN and Neuro Regression Methods in Mathematical Modeling. 4. Evaluation of R2 as a Model Assessment Criterion. 5. Questioning the Adequacy of Using Polynomial Structures. 6. The Effect of Using the Taguchi Method in Experimental Design on Mathematical Modeling. 7. Comparison of Different Test and Validation Methods Used in Mathematical Modeling. 8. Comparison of Different Model Assessment Criteria Used in Mathematical Modeling. 9. Comparison of the Effects of Experimental Design Methods on Mathematical Modeling. 10. Special Functions in Mathematical Modeling. 11. Conclusion.