Afdeling NUMA, Numerieke Analyse en Toegepaste Wiskunde
KU Leuven
Leuven, Belgium
4 uur geleden
source : OnlyEngineerJobs

NUMA is a research division within the Department of Computer Science of KU Leuven, with 12 permanent staff members and approximately 40 PhD and postdoctoral researchers.

NUMA (Numerical Analysis and Applied Mathematics) develops numerical algorithms and software for large-scale problems in science and engineering.

Its research ranges from algorithm design and analysis to software implementation and applications in other scientific and engineering domains.

Candidates must hold a Master in Mathematical Engineering or Applied Mathematics (or equivalent)Candidates should have a solid background in mathematics (e.

g., numerical methods for differential equations, simulation of stochastic processes and / or optimization).Candidates should have experience with programming of scientific software.

Excellent proficiency in English is required, as well as good communication skills, both oral and written.The aim of structural health monitoring is the on-line damage assessment of structures (bridges, buildings, towers, .

  • One of the most prominent techniques is vibration-based finite element (FE) model updating, where damage is identified as a local reduction in stiffness based on the modal characteristics of the structure (natural frequencies, mode shapes, ) Bayesian inference, for instance using Markov chain Monte Carlo (MCMC) methods is a natural computational framework to infer damage from measurement data while taking into account modeling and measurement uncertainty.
  • Current data assimilation techniques are usually limited to low-dimensional measurement data. A current challenge is therefore the development of model updating technologies that are able to handle full-field data obtained with high spatial resolution.

    Additionally, one should also take into account that the model itself is not perfect, and may contain modeling errors. In this project, the focus will be on the fundamental problems of developing a framework for Bayesian inference for FE model updating relying upon full-field data and imperfect models.

    We will consider an approach where the data is treated as a genuine field, rather than a vector with a limited number of entries.

    To reduce the computational cost of sampling of the field, multi-level strategy will be developed. In such a strategy, samples are taken on a hierarchy of low to high resolution approximations of the field.

    In the second half of the project, we will use the resulting methods on model problems stemming from structural health monitoring.

    The research will be carried out in an international team of numerical analysts at NUMA. The envisaged work will be in part generic (motivated by but not tied to a particular application), and in part applied to model problems in structural mechanics.

    The project will benefit from a fruitful collaboration with the Structural Mechanics Section of the Department of Civil Engineering.

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