Corso di “Computational Engineering Design Optimisation”, Prof. Timoleon Kipouros – Inizio 27.07.2021

Prof. Timoleon KIPOUROS is a Senior Research Associate in the Department of Engineering at the University of Cambridge and member of the Computational Design, and Process & Change Management research groups within the Cambridge Engineering Design Centre (EDC). He is also a part-time Lecturer on Computational Engineering Design Optimisation within the School of Aerospace, Transport and Manufacturing at the University of Cranfield since April 2017. He received a 5-year Diploma in Mechanical and Aeronautical Engineering from the University of Patras, Greece, in 2002, and his PhD in Multi-Objective Aerodynamic Design Optimisation from the University of Cambridge in 2006. He was also awarded a graduate certificate on Architecture and Systems Engineering from MIT in 2017. Since 2006, he worked as a Research
Associate and then as Senior Research Associate in Cambridge where he pioneered the development of a method for post-analysis of optimisation data and engineering design processes using a highly advanced interactive Parallel Coordinates approach. The method has been extended to support interactive computational design, and robust decision-making. He has more than 100 publications in international peer reviewed journals, conferences and industrial workshops and has supervised 21 PhD and 60 MSc projects.


Short PhD Course at DIMES, University of Calabria (online lectures)


Tuesday,   July 27, 2021: 15:00 - 17:00 (CEST)
Wednesday, July 28, 2021: 10:30 - 12:30 (CEST)
Thursday,  July 29, 2021: 10:30 - 12:30 (CEST)

MS Teams Meeting at:

Title: Computational Engineering Design Optimisation

The module aims to provide an understanding of optimisation theory and formulation of optimisation problems applied on engineering design. Understand the importance of the choice of suitable optimisation algorithms and complementary tools for geometry management and objective functions simulation and evaluation. Finally, to appreciate the importance of post-optimisation analysis and extraction of qualitative understanding of the relevant optimisation problems.

Intended Learning Outcomes:
On successful completion of this module a student should be able to:
1. Express the fundamental concepts of numerical and stochastic optimisation.
2. Identify the appropriate optimisation algorithms and formulate optimisation problems for a given engineering design study.
3. Perform assessment of an optimisation study.
4. Perform post-optimisation analysis and extract qualitative understanding of real-world design problems.