Maths for Design Optimisation: Introduction

Learn the Basics of Multidisciplinary Design Optimisation for Engineering Design

If you’ve heard about design optimisation or Multidisciplinary Design Optimisation (MDO) and wondered how it actually works in practice, this course is the ideal place to start.

In this beginner-friendly, hands-on course, you’ll build a solid foundation in the mathematics of design optimisation — the methodology that powers modern complex systems engineering. You’ll learn what optimisation really means, how optimisation problems are structured, and how engineers translate real design questions into mathematical formulations.

Starting from first principles, we’ll walk through the optimisation process, from defining objectives and constraints to visualising feasible regions and solution spaces. You’ll learn how to formulate optimisation problems clearly and correctly, explore how optimisation problems and algorithms are classified, and develop intuition for why some problems are easy to solve while others are not.

A key focus of this course is visual understanding. Rather than treating optimisation as a black box, you’ll build intuition by visualising and interacting with optimisation problems step by step. You’ll also get hands-on experience solving simple but important classes of problems, including linear and quadratic programming, to see how theory connects to real engineering applications.

Throughout the course, you’ll work through practical coding exercises based on real-world engineering problems such as optimisation for manufacturing production using Python, with quizzes to reinforce your understanding and help you check your progress.

By the end of this course, you’ll:

• Understand what optimisation is and how it fits into engineering design
• Be able to formulate optimisation problems with objectives and constraints
• Develop intuition through visualising optimisation problems and feasible regions
• Recognise different classes of optimisation problems and algorithms
• Gain hands-on experience with linear and quadratic programming using Plotly and Scipy
• Feel confident moving on to more advanced optimisation methods in later courses

This course is designed for engineers, students, and technical professionals who want to understand optimisation from the ground up — whether you’re preparing to use MDO, numerical optimisation tools, or advanced algorithms later on.

No prior optimisation experience is required. A basic familiarity with maths and programming is helpful, but everything you need is introduced step by step.

If you’re ready to build strong optimisation instincts and lay the groundwork for advanced design optimisation methods, this is where your journey begins.