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

Yu Chen, Edoardo Patelli, Zhen Yang, Adolphus Lye (2024). Towards robust prediction of material properties for nuclear reactor design under scarce data - a study in creep rupture property. In Proceedings of the 10th International Workshop on Reliable Engineering Computing, Beijing.

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, 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, Alice Cicirello, and Edoardo Patelli (2023). On-line Bayesian Inference for Structural Health Monitoring using Sequential Ensemble Monte Carlo Sampler. In Proceedings of the 13th International Conference on Structural Safety and Reliability, Shanghai. Link to paper: Click here 

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, 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 Quantication in Computational Sciences and Engineering, Athens. doi: 10.7712/120223.10345.19797

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

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, Ander Gray, and Edoardo Patelli (2021). Identication of Time-varying Parameters using Variational Bayes - Sequential Ensemble Monte Carlo Sampler. In Proceedings of the 31st European Safety and Reliability Conference, Angers. doi: 10.3850/978-981-18-2016-8_081-cd

Adolphus Lye, Alice Cicirello, and Edoardo Patelli (2020). Bayesian Model Updating of Reliability Parameters using Transitional Markov Chain Monte Carlo with Slice Sampling. In Proceedings of the 30th European Safety and Reliability Conference, Venice. doi: 10.3850/978-981-14-8593-0_4374-cd

Adolphus Lye, Alice Cicirello, and Edoardo Patelli (2019). A Review of Stochastic Sampling Methods for Bayesian Inference Problems. In Proceedings of the 29th European Safety and Reliability Conference, Hannover. doi: 10.3850/978-981-11-2724-3_1087-cd

Adolphus Lye, Alice Cicirello, and Edoardo Patelli (2019). Uncertainty Quantication of Optimal Threshold Failure Probability for Predictive Maintenance using Confidence Structures. In Proceedings of the 3rd International Conference on Uncertainty Quantication in Computational Sciences and Engineering, Crete. doi: 10.7712/120219.6364.18502

Adolphus Lye, Hector Diego Estrada-Lugo, and Edoardo Patelli (2019). Conversion of Fault Tree into Credal Network for Probabilistic Safety Assessment of a Nuclear Power Plant. In Proceedings of the 3rd International Conference on Nuclear Power Plants: Structures, Risk and Decommissioning, London. Link to paper: Click here 

Adolphus Lye, Theodore Kay Chen Ong, Sicong Xiao, and Keng Yeow Chung (2025). Physics-enhanced machine learning for probabilistic risk assessment in nuclear safety: An overview, recent developments, and perspectives. Annals of Nuclear Energy, 222, 111562. doi: 10.1016/j.anucene.2025.111562

Adolphus Lye, Wasin Vechgama, Mohamed 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 (Part of Special Collection: Uncertainty Quantification for Machine Learning in Engineering)

Adolphus Lye, Jathniel Chang, Sicong Xiao, and Keng Yeow Chung (2024). An Overview of Probabilistic Safety Assessment for Nuclear Safety: What Has Been Done, and Where Do We Go from Here? Journal of Nuclear Engineering, 5, 456-485. doi: 10.3390/jne5040029

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 (Part of Special Collection: Non-deterministic Model Updating and Structural Health Monitoring for Existing Structures)

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, 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 (Part of Special Issue: The NASA Langley Challenge on Optimization under Uncertainty)

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 (Part of Special Issue: Advances in Stochastic Model Updating)

Adolphus Lye (2023). Robust and Efficient Probabilistic Approaches towards Parameter Identification and Model Updating. University of Liverpool Repository. doi: 10.17638/03170546

Adolphus Lye (2018). Core Meltdown Simulation in Nuclear Power Plants. National University of Singapore, Faculty of Science, Department of Physics Undergraduate Thesis Repository. Download here.