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Mathematical Modeling in Science and Engineering focuses on using mathematics to describe, analyze, and solve real-world problems in various fields. It explains different types of models such as algebraic, statistical, optimization, and differential equation models. The book highlights both analytical and numerical methods, including the use of computational tools like MATLAB and Python. Applications are shown in physics, biology, engineering, and environmental science. It also covers modern trends like data-driven modeling and AI integration. Emphasis is given to model validation, sensitivity analysis, and practical case studies.
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Mathematical Modeling in Science and Engineering focuses on using mathematics to describe, analyze, and solve real-world problems in various fields. It explains different types of models such as algebraic, statistical, optimization, and differential equation models. The book highlights both analytical and numerical methods, including the use of computational tools like MATLAB and Python. Applications are shown in physics, biology, engineering, and environmental science. It also covers modern trends like data-driven modeling and AI integration. Emphasis is given to model validation, sensitivity analysis, and practical case studies.