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.
Please plan time before each Live Learning Session to review the relevant material and prepare questions or challenges from your own work.
Live Learning Sessions are offered twice on the scheduled days, at 09:00-10:30 and 15:00-16:30 CET. Both sessions cover the same content, so you only need to attend one of the two time slots. Each session includes approximately one hour of guided theory followed by 30 minutes to get support on the project, practice problems, or self-paced tutorials. Sessions will be recorded and made available afterwards, supporting participants who cannot attend live or who want to revisit the material.
The schedule over the four-week period is as follows:
Week 1, 5-9 October
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Tuesday 6 October: Live Learning Session on “Course introduction and what is optimization?”
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Thursday 8 October: Live Learning Session on “Optimization Problem Definition”
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Weekly self-paced practical tutorial: “Solving engineering design problems”
Week 2, 19-23 October
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Tuesday 20 October: Live Learning Session on “Connecting a simulation code to an optimizer”
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Thursday 22 October: Live Learning Session on “KKT Conditions”
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Weekly self-paced practical tutorial: “Solving engineering design problems”
Week 3, 26-30 October
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Tuesday 27 October: Live Learning Session on “Optimization Algorithms”
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Thursday 29 October: Live Learning Session on “Sensitivity Analysis”
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Weekly self-paced practical tutorial: “Solving engineering design problems”
Week 4, 2-6 November
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Thursday 5 November: Live Learning Session on advanced topics for future learning and questions on the project work
At the end of the course, you will submit a short project report based on your work with “Solving engineering design problems”.
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Familiarity with programming, e.g. Python, some basic understanding of calculus, linear algebra and a background in engineering or a related technical field.
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.
Self-paced activities include recorded lectures, readings, quizzes, exercises and project work. The course is supported by an online learning platform where materials, activities, recordings and practical tasks are made available.
Certificate of Attendance for all participants who complete the course.
Key Information
Meet your teachers
Learn from world-class researchers and passionate educators who bring cutting-edge expertise and hands-on experience into the classroom.
Michael Kenneth McWilliam
Senior Researcher
Michael Kenneth McWilliam is a Senior Researcher in the Department of Wind and Energy Systems at the Technical University of Denmark (DTU). An expert in system engineering, optimisation, aeroelastic modelling, and the development of advanced methodologies for wind turbine and wind farm analysis...
Course Modules
This course is designed around five thematic modules, with one key theme covered each week.
1.
Theoretical Tutorial on “What is optimization?”
2.
Theoretical Tutorial on “Optimization Problem Definition”
3.
Theoretical Tutorial on “KKT Conditions”
4.
Theoretical Tutorial on “Optimization Algorithms”
5.
Practical Tutorial on “Solving engineering design problems”
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.
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Mohit Sharma
Senior Vice Presiden at Marubeni Corporation
Singapore
“Solid and comprehensive overview!”
"For anyone considering a career transition, this course offers a solid and comprehensive overview of wind energy. It equips you with the essential knowledge and skills needed to smoothly transition into the wind energy field."
Sophie Yin
Renewables Engineer, Woodside Energy
Australia
“Flexible learning with outstanding support!”
“What I enjoy the most about the programme is the flexibility. I have been able to access the content around other aspects of my life, which has been very valuable. Also, the lecturers have made them available to the students online to answer questions and actually provide knowledge beyond the course, which I really enjoyed.”
Fahd Outailleur
Head of Engineering at Enel Green Power
Morocco
“Europe’s leader in wind energy”
“I chose to enrol in this wind energy master's program because DTU is the leading technical university in Europe, renowned for its specialization in wind energy. The program reflects the state of the art of the sector. Before joining, I knew some of their software programmes like WAsP and the Global Wind Atlas. So I was confident about the quality of the training.”
Testimonials
Discover what DTU students have to say about their journey, their experiences, and the skills they’ve gained.
Swipe left to see more
Financing Options
We offer a range of discounts and packages to make the programme more accessible.
Early Bird Discount
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20%
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10%
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Team-Based Learning
Enroll a group of 3 or more participants from the same company and benefit from special team pricing.
Boost collaboration and apply knowledge directly in your workplace.
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About the Course
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.
Who is this course for?
It is designed for engineers who want to understand how optimization can be used to both solve problems and gain deeper insight into the design problem.
Pre-register here
For course-specific questions or if you are looking for a customised training solution for your company, please contact us at courses@windenergy.dtu.dk.
Register here :)
For course-specific questions or if you are looking for a customised training solution for your company, please contact us at courses@windenergy.dtu.dk.
After this course, you can
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