27 – 28 July 2026

Institut Teknologi Bandung (ITB), Bandung, Indonesia

Overview

Engineering design optimization has become a central tool in modern engineering, enabling researchers and practitioners to systematically improve the performance of complex systems. With the rapid development of computational methods, optimization methods are now capable of addressing large-scale, multidisciplinary, and highly nonlinear problems.

This two-day short course provides an introduction to modern engineering design optimization, combining fundamental theory with practical hands-on session. The course covers classical optimization methods, multi-objective optimization, Bayesian optimization, topology optimization, robust design optimization, multidisciplinary optimization, and machine learning approaches for engineering design.

Participants will gain both conceptual understanding and practical experience through hands-on tutorials, allowing them to implement optimization algorithms and apply them to engineering and scientific problems.

Learning Objectives

  • Understand the fundamental principles of engineering design optimization
  • Apply classical optimization algorithms to engineering problems
  • Formulate and solve multi-objective optimization problems
  • Implement optimization algorithms using Python
  • Understand the role of machine learning in engineering design
  • Apply Bayesian optimization for expensive engineering simulations
  • Understand the basics of topology optimization
  • Incorporate uncertainty quantification and robustness in engineering design
  • Understand the principles of multidisciplinary design optimization (MDO) and its role in complex engineering system design

Topics Covered

  • Overview of engineering design optimization
  • Mathematical optimization methods
  • Multi-objective optimization
  • Machine learning for engineering design
  • Bayesian optimization
  • Topology optimization
  • Robust design optimization and uncertainty quantification
  • Multidisciplinary design optimization (MDO)

Instructors

Joaquim R. R. A. Martins

Pauline M. Sherman Collegiate Professor of Aerospace Engineering
University of Michigan, USA

Joseph Morlier

Professor in Multidisciplinary Design Optimization
Institut supérieur de l’aéronautique et de l’espace (ISAE-SUPAERO), France

Koji Shimoyama

Professor Department of Mechanical Engineering
Kyushu University, Japan

Pramudita Satria Palar

Assistant Professor
Faculty of Mechanical and Aerospace Engineering
Institut Teknologi Bandung, Indonesia

Rhea Liem

Reader in Sustainable Aviation
Department of Aeronautics
Imperial College London, United Kingdom

Yohanes Bimo Dwianto

Assistant Professor
Faculty of Mechanical and Aerospace Engineering
Institut Teknologi Bandung, Indonesia

Dajung Kim

Enseignante-chercheur
ENAC, France – École Nationale de l’Aviation Civile

Target Audience

  • Graduate students and final-year undergraduate students in engineering
  • Researchers working in computational engineering
  • Engineers interested in design optimization
  • Practitioners interested in machine learning for engineering design

Registration

Registration for the short course can be completed through the online form available at:
Registration Here

Due to limited capacity, the number of participants is restricted. Applicants will be selected based on the merit of their application  

Registration Deadline: 23:59 (Western Indonesia Time), 24 May 2026.

Contact:

For inquiries regarding the short course, please contact:

Pramudita Satria Palar, Ph.D.
Assistant Professor, Faculty of Mechanical and Aerospace Engineering
Institut Teknologi Bandung (ITB), Bandung, Indonesia
Email: pramsp@itb.ac.id