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
Background
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.
Lecturers
The University of New South Wales, Australia
Evolutionary Algorithms,
Multi-objective optimization, Metaheuristics
Institut Supérieur De L’aéronautique Et De L’espacec (ISAE-SUPAERO), France
Topology optimization, structural optimization, multidisciplinary design optimization
University of Colorado Boulder, USA
Physics-informed Neural Network, Applied Mathematics, Statistics
Hong Kong University of Science and Technology, Hong Kong
Aircraft design, surrogate modeling, aviation and flight engineering
Saint Joseph’s University, USA
Data-driven modeling, operation research, numerical optimization
Institut Teknologi Bandung, Indonesia
Surrogate modeling, machine learning, aerodynamic optimization
Office national d’études et de recherches aérospatiales (ONERA), France
Aircraft Design, Bayesian optimization, Multidisciplinary Design Optimization
University of Cambridge, UK
Statistical finite element method, aeroelasticity
Institute of Fluid Science Tohoku University, Japan
Optimization methods for aerospace and mechanical engineering, surrogate model
Institut Teknologi Bandung, Indonesia
Particle-based simulation methods, numerical simulation for optimization
University of Trieste, Italy
Optimization under uncertainty, optimization in fluid mechanics
University of Electro-Communications , Japan
Fluid informatics, Evolutionary computation, Aerospace design
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
Assignments
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
Examinations
Student’s performance evaluation will be done using quizzes and exams
