Ids 338J Multidisciplinary System Design Optimization Spring 2010

Modern engineering challenges require a holistic approach to system design, where individual components must work in perfect harmony. This course introduces the foundational principles of multidisciplinary design optimization, bridging the gap between creative design concepts and rigorous mathematical optimization. You will learn how to approach complex engineering problems by evaluating performance, cost, and lifecycle value simultaneously. By completing this written course, you will transition from designing isolated components to optimizing entire interconnected systems. You will gain the skills to formulate design optimization problems, handle competing objectives, and apply modern algorithmic tools to find balanced, high-value engineering solutions. What you'll learn: - Understand the core concepts of multidisciplinary design optimization and system-level modeling - Formulate design optimization problems with multiple design variables and operational constraints - Apply quantitative tools to evaluate tradeoffs between system performance, cost, and lifecycle value - Configure optimization algorithms suitable for complex, non-linear engineering systems - Practice decomposing large-scale systems into manageable, coupled subsystems for analysis - Learn to integrate modern data-driven design variables and simulation feedback into your optimization workflow This course begins with essential definitions, system architecture concepts, and the mathematical foundations of optimization. You will then explore objective functions, constraint handling, and subsystem coupling before moving on to practical formulation strategies and modern algorithmic solutions. This course is designed for beginner-to-intermediate engineers, system architects, and technical project leads who want to build a structured, quantitative foundation in system-level design optimization. No prior optimization experience is required. Start reading today to master the quantitative tools needed to design next-generation engineering systems.