Course Overview
This course introduces numerical optimization as a practical method for solving engineering design problems and gaining deeper insight into how design choices affect system performance. It is designed for engineers and technical professionals who want to use optimization not only to identify improved solutions, but also to understand the trade-offs, constraints and sensitivities behind them.
Many engineering challenges - particularly within wind energy systems - involve balancing performance, cost, feasibility and technical constraints. Through this course, you will learn how to formulate these challenges as optimization problems, define objectives and constraints, and connect computational models to optimization tools.
The course combines guided theory Live Sessions with hands-on, self-paced practical work. You will work with realistic engineering examples, complete preparatory activities such as readings, quizzes and exercises, and apply the methods in a short project. By the end of the course, you will be able to carry out basic optimization studies independently and interpret the results to support better design decisions.
Course Highlights
Here are the key highlights of the program, designed to give you a clear picture of what sets this course apart and how it can strengthen your expertise in wind energy.
Practical optimization skills - Apply numerical optimization methods to realistic engineering design problems.
Design trade-offs - Explore trade-offs between performance, cost and constraints in engineering systems.
Hands-on project work - Work with computational models and connect them to optimization tools.
Wind energy relevance - Use examples and challenges that connect optimization to wind energy systems.
Deeper system insight - Interpret optimization results to understand system behaviour and improve design decisions.
MAIN GOAL
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Learning Outcomes
After completion of this course, you will gain the ability to:
- + Formulate engineering design problems as optimization problems by defining objective functions, design variables and constraints.
- + Apply optimization algorithms by coupling them to computational analysis tools.
- + Interpret optimization results to gain insights into engineering design problems.
- + Identify different properties of optimization problems and classify them into different types.
- + Select and evaluate appropriate optimization techniques for a given problem, based on that problems classification
Meet Your Instructors
Senior Researcher
Admissions
Entry Requirements
- + Familiarity with programming, e.g. Python, some basic understanding of calculus, linear algebra and a background in engineering or a related technical field.
Teaching and Assessment Methods
- + Self-Paced Activities:
- Lectures
- Readings
- Quizzes
- Exercises
- Project Work - + Scheduled Live Learning Sessions
Application Deadline: 05/10/2026.
Practical Notes
- + The total time commitment is approximately 40 hours across the four-week course. Each week includes self-paced preparation through recorded lectures, readings, quizzes, exercises and project work.
- + This online course is designed as a flexible, guided learning journey over four weeks. You will combine self-paced preparation with scheduled Live Learning Sessions, so you can study around your work while still having regular opportunities to interact with the teacher and other participants.
- + Please plan time before each Live Learning Session to review the relevant material and prepare questions or challenges from your own work.
Fees & Funding
Tuition Fees
Course Fee: 1000 EUR
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Course Info
Contact course information for any additional information relating to this course.
