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Design Optimization

Research group Design optimization is based on the idea of exploiting the power of computer simulations and optimization in the engineering design process.

Engineering design is about functional characteristics: Can we build lighter mechanical devices without compromising structural properties? How can we design antennas, such as these in our smartphones, to send and recieve information as efficiently as possible? Are the better ways than traditional rules of thumb to design intricate details in loudspeaker components?  Can we improve the design of other acoustic devices such as mufflers and damping structures in noisy environments? Is it possible to reduce the aerodynamic drag on, for instance, an airplane wing by altering its surface structure to move downstream the point at which the airflow becomes turbulent?

All these are applications that have been addressed in the research developed by the Design Optimization Group. The foundational principle is to combine computer simulations with gradient-based optimization algorithms in a kind of learning methodology. The algorithms successively improves an initial design layout using the data given by computer simulations. A key ingredient is the use of adjoint variables, a techique also crucial in Machine Learning, where it is known as back propagation. This technique allows the efficient simultaneous handling of thousands, millions, and, recently, even billions of design variables. Therefore, these algorithms can handle design complexity beyond what is possible by manual means or trial and error.

The research carried out by the group concerns methods development as well as innovative applications. In the methods development, we aim for a vertical perspective, addressing a whole chain of issues: mathematical modeling of the considered systems, discretization and implementation on modern computing hardware, execution, analysis, post processing, prototype design, and experimental verification. Although we have covered the whole chain in some projects, most research focuses on the initial parts of the chain. Since we aim for novel methods, the software that is needed is usually developed within the research group itself. For the use of the methods in innovative applications, we cooperate with external partners that contribute system knowledge in the application under  consideration.                

Research leaders

Eddie Wadbro
Visiting professor
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Emadeldeen Hassan
Associate professor
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Overview

Participating departments and units at Umeå University

Department of Computing Science

Research area

Computing science

Latest publications

Journal of the Acoustical Society of America, Acoustical Society of America 2024, Vol. 155, (1) : 742-756
Mousavi, Abbas; Berggren, Martin; Hägg, Linus; et al.
Structural and multidisciplinary optimization (Print), Springer Nature 2024, Vol. 67, (5)
Mousavi, Abbas; Uihlein, Andrian; Pflug, Lukas; et al.
Robotics and Autonomous Systems, Elsevier 2024, Vol. 179
Wiberg, Viktor; Wallin, Erik; Fälldin, Arvid; et al.
Structural and multidisciplinary optimization (Print), Springer Nature 2024, Vol. 67, (8)
Setta, Mario; Hägg, Linus; Wadbro, Eddie
Automation, MDPI 2024, Vol. 5, (3) : 259-281
Aoshima, Koji; Fälldin, Arvid; Wadbro, Eddie; et al.
Computers and Electronics in Agriculture, Elsevier 2023, Vol. 212
Hosseini, S. Ahmad; Wadbro, Eddie; Ngoc Do, Dung; et al.
Journal of Computational Physics, Elsevier 2023, Vol. 472
Araujo-Cabarcas, Juan Carlos; Engström, Christian; Wadbro, Eddie
Computer Methods in Applied Mechanics and Engineering, Elsevier 2023, Vol. 403
Nobis, Harrison; Schlatter, Philipp; Wadbro, Eddie; et al.
Materials & design, Elsevier 2023, Vol. 234
Mousavi, Abbas; Berggren, Martin; Wadbro, Eddie
Structural and multidisciplinary optimization (Print), Springer Nature 2023, Vol. 66
Bokhari, Ahmad Hasnain; Hassan, Emadeldeen; Wadbro, Eddie

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