PhD position on infinite-dimensional Bayesian inversion of physics-based models

This PhD position is supervised by Prof. Geert Lombaert (Department of Civil Engineering), and co-supervised by Prof. Giovanni Samaey (Department of Computer Science). The position is based in the Structural Mechanics Section of the Department of Civil Engineering at the University of Leuven (KU Leuven). The Department of Civil Engineering is nicely located in and around the Arenberg Castle on Campus Arenberg in Heverlee, one kilometre from Leuven’s city center.
The research is part of the larger project “Computational methods for infinite-dimensional Bayesian inversion of physics-based models in engineering applications” led by Prof. Giovanni Samaey, Prof. Geert Lombaert, Prof. Dirk Nuyens and Prof. Johan Meyers. The research will be done in close collaboration with 5 other PhD students funded on the project, as well as researchers working on complementary related projects.
Research in the Structural Mechanics Section (https://www.kuleuven.be/bwm) of the Department of Civil Engineering focuses on the static and dynamic analysis of structures and is structured along six research lines: vibrations in the built environment, structural identification and evaluation, building acoustics, shape and topology optimization, computational structural engineering and human-induced vibration of civil engineering structures. The PhD position fits in the second line where the section has a long standing tradition in research on Structural Health Monitoring.
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Project

This PhD project aims at the development of computational methods for infinite-dimensional Bayesian inversion of physics-based models in structural engineering. The work is motivated by recent developments in high-resolution sensing and monitoring of structures, which provide near full-field data (strains, accelerations, displacements). Such data can be used to identify parameters of models of structures (typically a finite element model), but also the dynamic loads acting upon the structure, as well its state (displacement and velocity at every degree of freedom). Since parameters such as the stiffness or loads depend on position and time, these represent field variables, explaining the need to address the identification or inversion in an infinite-dimensional setting. Currently available methods are not adapted to this problem setting and typically only working well for a small, finite number of variables to be identified. The choice for Bayesian inversion is motivated by the fact that the Bayesian framework allows accounting for measurement and model uncertainty, by identifying a posterior distribution of the sought after variables as opposed to a single value.

The target of the overarching project is the development of Bayesian computational methods for these inverse problems and aims both at increasing their validity, and at reducing their computational cost. As a prototypical system with a lot of future and wider application potential, the monitoring (structural health of) structures (such as buildings or wind turbines) excited by wind loads are considered. The focus of this PhD project is the development of methods tailored to a context of structural engineering.

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Offer

Interested?

For more information please contact Prof. dr. ir. Geert Lombaert, email: geert.lombaert@kuleuven.be.

As part of your application, we expect the following: (1) motivation letter, (2) curriculum vitae  (3) transcript of records (BSc and MSc degrees), (4) copy of diplomas (BSc and MSc degrees), (5) a sample of technical writing in English, e.g. a paper with you as main author, or your bachelor or master thesis, (6) proof of proficiency in English (TOEFL or IETLS certificate or equivalent proof), and (7) contact details of three persons who can provide a reference on our request (so please do not include reference letters, only provide contact details).

We request a TOEFL ibt total score of 94, with no section scores below 22, or an IELTS overall band score of 7.0, with no band scores below 6.5.

Pre-selected candidates will be invited for an online interview.

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