Jasmin Jelovica

Assistant Professor

Research Classification

Research Interests

Finite element analysis
Metals and Alloys
Production and Process Optimization
Sandwich structures
Solid Mechanics
Stress Analysis
Structural optimization
Ultimate, fatigue and impact strength
Welding and joining of metals

Relevant Degree Programs

Affiliations to Research Centres, Institutes & Clusters

Research Options

I am available and interested in collaborations (e.g. clusters, grants).
I am interested in and conduct interdisciplinary research.
I am interested in working with undergraduate students on research projects.

Research Methodology

Finite element analysis
machine learning


Doctoral students
Any time / year round

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

I support public scholarship, e.g. through the Public Scholars Initiative, and am available to supervise students and Postdocs interested in collaborating with external partners as part of their research.
I support experiential learning experiences, such as internships and work placements, for my graduate students and Postdocs.
I am open to hosting Visiting International Research Students (non-degree, up to 12 months).

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Postdoctoral Fellows

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.

Adaptive repair method for constraint handling in multi-objective optimization based on constraint-variable relation (2020)

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.

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