Adolphus Lye, Institute for Risk and Uncertainty, University of Liverpool, UK
Robust and Efficient Probabilistic Approaches towards Parameter Identification and Model Updating (2022)
Supervisors: Prof. Dr. Edoardo Patelli (University of Strathclyde, UK); Prof. Dr. Alice Cicirello (TU Delft, The Netherlands)
Keywords: Bayesian Model Updating, Markov Chain Monte Carlo, Sequential Monte Carlo, Affine-invariance, Model Uncertainty, Structural Dynamics, Artificial Neural Networks, Nuclear
The dissertation provides an in-depth introduction on the concept of Bayesian model updating, a detailed review on the Monte Carlo sampling techniques employed within the discipline, the proposed sampling techniques in the form of the Transitional Ensemble Markov Chain Monte Carlo and the Sequential ensemble Monte Carlo samplers, and the prescriptions aimed towards addressing some of the research gaps identified within the topic, and suggestions on some of the open research questions that remains to be investigated. As an additional selling-point, and in the spirit of reproducibility in Science, the MATLAB codes to the sampling algorithms, numerical examples, and the experimental examples presented in the dissertation are freely available on GitHub to which the links are provided in the document. Each chapter is based on a peer-reviewed work, with the exception of Chapters 1 and 10.
More information on Adolphus work
References and links:
- Adolphus Lye (2023). Robust and Efficient Probabilistic Approaches towards Parameter Identification and Model Updating. University of Liverpool Repository. doi: 10.17638/03170546
- Adolphus Lye, Ander Gray, Marco de Angelis, and Scott Ferson (2023). Robust Probability Bounds Analysis for Failure Analysis under Lack of Data and Model Uncertainty. In Proceedings of the 5th ECCOMAS Thematic Conference on Uncertainty Quantification in Computational Sciences and Engineering, Athens.
- Adolphus Lye, Luca Marino, Alice Cicirello, and Edoardo Patelli (2023). Sequential Ensemble Monte Carlo Sampler for On-Line Bayesian Inference of Time-Varying Parameter In Engineering Applications. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part B: Mechanical Engineering, 9, 031202. doi: 10.1115/1.4056934
- Adolphus Lye, Nawal Prinja, and Edoardo Patelli (2022). Probabilistic Artificial Intelligence Prediction of Material Properties for Nuclear Reactor Designs. In Proceedings of the 32nd European Safety and Reliability Conference, Dublin. doi: 10.3850/978-981-18-5183-4_S24-02-306-cd
- Adolphus Lye, Masaru Kitahara, Matteo Broggi, and Edoardo Patelli (2022). Robust optimisation of a dynamic Black-box system under severe uncertainty: A Distribution-free framework. Mechanical Systems and Signal Processing, 167, 108522. doi: 10.1016/j.ymssp.2021.108522
- Adolphus Lye, Alice Cicirello, and Edoardo Patelli (2022). An efficient and robust sampler for Bayesian inference: Transitional Ensemble Markov Chain Monte Carlo. Mechanical Systems and Signal Processing, 167, 108471. doi: 10.1016/j.ymssp.2021.108471