Relevant Degree Programs
Affiliations to Research Centres, Institutes & Clusters
1) New finite element analysis techniques for lightweight structures;
2) Development of optimization algorithms for structural optimization;
3) Machine learning methodology for reduced order modeling of mechanical systems.
Several courses on numerical analysis techniques in solid mechanics, mechanics of materials, structural vibrations, optimization and machine learning.
Experience using programming languages (Python, Matlab) for numerical analysis and/or optimization
Research experience on the above topics, possibily with journal papers published
Complete these steps before you reach out to a faculty member!
- Familiarize yourself with program requirements. You want to learn as much as possible from the information available to you before you reach out to a faculty member. Be sure to visit the graduate degree program listing and program-specific websites.
- Check whether the program requires you to seek commitment from a supervisor prior to submitting an application. For some programs this is an essential step while others match successful applicants with faculty members within the first year of study. This is either indicated in the program profile under "Admission Information & Requirements" - "Prepare Application" - "Supervision" or on the program website.
- Identify specific faculty members who are conducting research in your specific area of interest.
- Establish that your research interests align with the faculty member’s research interests.
- Read up on the faculty members in the program and the research being conducted in the department.
- Familiarize yourself with their work, read their recent publications and past theses/dissertations that they supervised. Be certain that their research is indeed what you are hoping to study.
- Compose an error-free and grammatically correct email addressed to your specifically targeted faculty member, and remember to use their correct titles.
- Do not send non-specific, mass emails to everyone in the department hoping for a match.
- Address the faculty members by name. Your contact should be genuine rather than generic.
- Include a brief outline of your academic background, why you are interested in working with the faculty member, and what experience you could bring to the department. The supervision enquiry form guides you with targeted questions. Ensure to craft compelling answers to these questions.
- Highlight your achievements and why you are a top student. Faculty members receive dozens of requests from prospective students and you may have less than 30 seconds to pique someone’s interest.
- Demonstrate that you are familiar with their research:
- Convey the specific ways you are a good fit for the program.
- Convey the specific ways the program/lab/faculty member is a good fit for the research you are interested in/already conducting.
- Be enthusiastic, but don’t overdo it.
G+PS regularly provides virtual sessions that focus on admission requirements and procedures and tips how to improve your application.
Graduate Student Supervision
Master's Student Supervision
Theses completed in 2010 or later are listed below. Please note that there is a 6-12 month delay to add the latest theses.
Evolutionary algorithms are popular tools for optimization of both theoretical and real-world problems due to their ability to perform global search and to deal with non-convex, multi-objective problems. Handling the constraints is a major concern in optimization that can prolong the search or prevent the algorithm from convergence. Common approaches for constraint handling usually discard or devalue infeasible solutions, losing the valuable information they carry. Alternatively, common repair methods for constraint handling are limited to specific problem types. This study focuses on the development of a repair method for constraint handling in multi-objective optimization.A generic approach is proposed for improving the constraint handling. The method identifies infeasible solutions with high-quality objective values or small constraint violations. These solutions are modified to make them feasible while preserving their good position in the objective space. The repair is performed based on the relationship between constraints and variables in the problem. Variables causing infeasibility are replaced with values from other solutions. The number of repaired solutions varies during optimization. The remaining part of the solution set is created by usual operators to preserve the diversity and normal procedure of the algorithm.The proposed repair method is applied to NSGA-II as one of the most commonly used multi-objective algorithms. The algorithm is tested on an optimization benchmark test case and an engineering optimization problem involving the structural design of a product tanker. The performance of the proposed approach is compared to the original algorithm and a few other constraint handling methods. Also, a competitive evolutionary algorithm, MOEA/D, is used for validation of the results. The proposed method showed faster convergence to the Pareto frontier and better diversity, covering the highly constrained regions of the design space. Additionally, the proposed algorithm was successful in reaching feasible solutions much faster, which is important in the case of computationally expensive problems, a common situation in engineering.