Multidisciplinary Optimization and Machine Learning for Engineering Design International virtual Course
19 July 2021 - 5 august 2021

Recent News

19 June 2021

23 June 2021

23 June 2021

10 July 2021

Please be advised that the Multidisciplinary  Optimization and Machine Learning for Engineering Design virtual course applications has been closed and selected participants will be announced on 23 June 2021.

Kazuhisa Chiba from the University of Electro-Communications , Japan, has joined us as a lecturer to deliver a talk on real-world applications of optimization algorithms

The selected participants for Multidisciplinary  Optimization and Machine Learning for Engineering Design virtual course has been announced through this link

Joaquim R. R. A. Martins from the University of Michigan, USA, has joined us as a lecturer to deliver a talk on real-world optimizations.

About This Course


There is an increasing demand for engineers to improve the design of engineering products to stay competitive with competitors, especially in the current era of high-performance computing and abundant data. Engineers then resort to optimization techniques to help them find high-performance solutions and uncover salient design insight and features. Optimization techniques have been constantly evolving to adapt to modern engineering practices, challenges and complexities.  Especially in this age of data, mastery of optimization for engineering design has never been more important than before. The dawn of machine learning has also enabled a more efficient data-driven optimization by aiding optimization techniques to rapidly discover insight and knowledge from data. The intertwine between computer simulations, experimental data, and data-driven methods is now one of the building blocks of modern engineering. Fluency in optimization  and machine learning is then becoming an important skill that must be possessed by students, practitioners, and researchers in design optimization to take advantage of the abundant amount of data.

Why you should join this course?

This international virtual course (IVC) aims to equip students with basic and advanced introduction to multidisciplinary design optimization and machine learning. This course covers the introduction, important topics, and practical aspects of optimization and machine learning for engineering design. In this course, students will (1) learn the basic of optimization and how to formulate engineering design optimization problems, (2) learn various techniques that support engineering optimization (e.g. uncertainty quantification and data mining), (3) learn the complexities and challenges in deploying optimization techniques for real-world applications, (4) learn the basic of machine learning in the context of engineering design optimization, (5) learn how to use various optimization techniques (gradient-based, gradient-free, machine learning) to solve the formulated problems. Students will learn the theory and practice of optimization from world-class researchers and also through Python-based tutorials guided by tutors.


Hemant Kumar Singh

The University of New South Wales, Australia

Evolutionary Algorithms,
Multi-objective optimization, Metaheuristics

Joseph Morlier

Institut Supérieur De L’aéronautique Et De L’espacec (ISAE-SUPAERO), France

Topology optimization, structural optimization, multidisciplinary design optimization

Maziar Raissi

University of Colorado Boulder, USA

Physics-informed Neural Network, Applied Mathematics, Statistics

Rhea Patricia Liem

Hong Kong University of Science and Technology, Hong Kong

Aircraft design, surrogate modeling, aviation and flight engineering

Rommel Regis

Saint Joseph’s University, USA

Data-driven modeling, operation research, numerical optimization

Pramudita Satria Palar

Institut Teknologi Bandung, Indonesia

Surrogate modeling, machine learning, aerodynamic optimization

Nathalie Bartoli

Office national d’études et de recherches aérospatiales (ONERA), France

Aircraft Design, Bayesian optimization, Multidisciplinary Design Optimization

Joel Henry

Monolith AI, UK

Composite structures, AI-based engineering optimization

Eky Valentian Febrianto

University of Cambridge, UK

Statistical finite element method, aeroelasticity

Koji Shimoyama

Institute of Fluid Science Tohoku University, Japan

Optimization methods for aerospace and mechanical engineering, surrogate model

Lavi Rizki Zuhal

Institut Teknologi Bandung, Indonesia

Particle-based simulation methods, numerical simulation for optimization

Lucia Parussini

University of Trieste, Italy

Optimization under uncertainty, optimization in fluid mechanics


University of Electro-Communications , Japan

Fluid informatics, Evolutionary computation, Aerospace design

Name: Joaquim Martins
Uniqname: jrram
Department: AERO

Photo: Joseph Xu, Michigan Engineering Communications & Marketing

University of Michigan , USA

Multidisciplinary Design Optimization, Adjoint Methods, Aerodynamic Shape Optimization

This international virtual course is jointly organized by The Faculty of Mechanical and Aerospace Engineering at Institut Teknologi Bandung, in collaboration with Tohoku University (Japan) and The Hong Kong University of Science and Technology (Hong Kong).

Institution Partners

Supported By

Course Poster

Daily Lectures

Course lectures will be given by  worldly renowned professors/researchers


Course assignments will be given to complement the daily lectures

Coding Tutorials

This course also includes hands on coding tutorials to solve engineering optimization problems


Student’s performance evaluation will be done using quizzes and exams

Course Activities

Important Dates