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Foundations of Mathematical Modeling and Analysis for Engineering

  • Book

  • September 2025
  • Elsevier Science and Technology
  • ID: 6057643

Foundations of Mathematical Modeling and Analysis for Engineering is designed for first-year graduate and advanced undergraduate engineering students. The book explores linear system theory and demonstrates its application in developing analytical solutions to various equations, essential for describing physical systems through mathematical modeling. This foundation is crucial for learning and research in engineering and various scientific fields. It equips students with the mathematical tools needed to solve entire classes of linear algebraic, ordinary-, and partial-differential equations, while also teaching principles for formulating, organizing, and solving linear subsystems, all of which are vital components of both linear and nonlinear mathematical models.

This knowledge prepares students for advanced studies in engineering, applied mathematics, and foundational sciences.

Table of Contents

  1. Introduction
  2. Mathematical representations of physical phenomena
  3. Solving linear algebraic equations
  4. Vector spaces and their representations
  5. Linear transformations and representations
  6. Inner product spaces
  7. Operators and matrix representations
  8. Ordinary differential equations
  9. Function representation and transforms
  10. Partial differential equations
  11. System and parameter identification

Authors

Ted A Watson Department of Chemical and Biological Engineering, Colorado State University, Fort Collins, CO, USA. Professor Watson holds a BS from the University of Texas at Austin and a PhD from California Institute of Technology, both in chemical engineering. He attained the position of Professor at Texas A&M University (TAMU) and is now at Colorado State University (CSU), which he joined as founding department head of chemical & biological engineering. Throughout his career, he has developed methods for mathematical modeling and system and parameter identification, working with important systems ranging from quantum to geological scales. He established and directed the Engineering Imaging Laboratory at TAMU and Rocky Mountain Magnetic Resonance at CSU and was recently elected Fellow of the American Institute of Chemical Engineers (AIChE).