Framework

The research works embarked under the R2UQ framework contribute to the following United Nations Sustainable Development Goals:

The Risk, Reliability and Uncertainty Quantification (R2UQ) research framework was conceptualised in the fall of 2023 as a result of a series of reflections on my research journey while authoring and revising my PhD Thesis. There are two aspects to the R2UQ framework - the Philosophical and the Technical aspects.

The research philosophy is based upon the three non-negotiable values pertaining to research conduct:

On the Technical aspect, the R2UQ framework encompasses the following disciplines:

Risk analysis;
Reliability analysis; and
Uncertainty quantification

with applications including (not limited to):

Structural engineering;
Aerospace engineering;
Nuclear engineering;
Asset management for Predictive maintenance; and
Probabilistic risk analysis

The motivation behind the R2UQ framework stems from the following three grand challenges faced within the field of engineering:

For this reason, there are three research objectives which the R2UQ framework seeks to achieve:

To develop robust machine-learning approaches towards parameter identification and model calibration under limited information;
To study and develop appropriate metrics to quantify model performance under model form uncertainty; and
To develop robust probabilistic bounds analysis framework for forward propagation under uncertain dependencies towards risk and reliability quantification.

Based on these objectives, the research focus is classified into four distinct themes:

Theme 1: Data-driven stochastic model updating and calibration

Research focus: Development of robust numerical techniques towards characterising the uncertainty of the input parameter(s) and to calibrate models under limited information.

Key publications:

Adolphus Lye, and Luca Marino (2023). An investigation into an alternative transition criterion of the Transitional Markov Chain Monte Carlo method for Bayesian model updating. In Proceedings of the 33rd European Safety and Reliability Conference, Southampton. doi: 10.3850/978-981-18-8071-1_P331-cd
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
Adolphus Lye, Alice Cicirello, and Edoardo Patelli (2021). Sampling methods for solving Bayesian model updating problems: A tutorial. Mechanical Systems and Signal Processing, 159, 107760. doi: 10.1016/j.ymssp.2021.107760

Theme 2: Data-informed model selection and averaging

Research focus: Development numerical techniques towards performing model selection and model averaging under limited information.

Key publications:

Michael McGurk, Adolphus Lye, Ludovic Renson, and Jie Yuan (2024). Data-Driven Bayesian Inference for Stochastic Model Identification of Nonlinear Aeroelastic Systems. AIAA Journal. doi: 10.2514/1.J063611
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, Alice Cicirello, and Edoardo Patelli (2022). On-line Bayesian Model Updating and Model Selection of a Piece-wise model for the Creep-growth rate prediction of a Nuclear component. In Proceedings of the 8th International Symposium on Reliability Engineering and Risk Management, Hannover. doi: 10.3850/978-981-18-5184-1_MS-02-208-cd

Theme 3: Forward uncertainty propagation and interval analysis

Research focus: Development of numerical methods towards propagating imprecise model inputs through a model and yielding meaningful uncertain outputs to quantify the system performance/reliability.

Key publications:

Adolphus Lye, W. Vechgama, M. Sallak, S. Destercke, Scott Ferson, and Sicong Xiao (2025). Advances in the Reliability Analysis of Coherent Systems under Limited Data with Confidence Boxes. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part A: Civil Engineering, 11, 04024074. doi: 10.1061/AJRUA6/RUENG-1380
Adolphus Lye, Scott Ferson, and Sicong Xiao (2024). Distribution-free stochastic model updating for the Physics-guided reliability analysis of a material thermal property under limited data. In Proceedings of the 17th Probabilistic Safety Assessment and Management and Asian Symposium on Risk Assessment and Management 2024, Sendai. Link to paper: Click here
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 International Conference on Uncertainty Quantification in Computational Sciences and Engineering, Athens. doi: 10.7712/120223.10345.19797

Theme 4: Model Verification and Validation

Research focus: Development and the study of appropriate statistical distance functions to quantify how well a model agrees with the physics (verification) and how applicable is it in predicting the actual phenomenon (validation).

Key publications:

Adolphus Lye, Scott Ferson, and Sicong Xiao (2025). A Hellinger Distance-Based Stochastic Model Updating Framework for the Accreditation Validation of a Material Thermal Property Under Limited Data. In Proceedings of the 35th European Safety and Reliability Conference and 33rd Society for Risk Analysis Europe Conference, Stavanger. doi: 10.3850/978-981-94-3281-3_ESREL-SRA-E2025-P4632-cd
Adolphus Lye, Scott Ferson, and Sicong Xiao (2024). Stochastic Model Updating Using Jenson-Shannon Divergence For Calibration And Validation Under Limited Data. In European Safety and Reliability Conference 2024 Monograph Book Series, Part 1: Accident and Incident Modelling & Uncertainty Analysis, Krakow. Link to paper: Click here
Adolphus Lye, Scott Ferson, and Sicong Xiao (2024). Comparison between distance functions for Approximate Bayesian Computation towards Stochastic model updating and Model validation under limited data. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part A: Civil Engineering, 10, 03124001. doi: 10.1061/AJRUA6.RUENG-1223

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