An investigation into an alternative transition criterion of the Transitional Markov Chain Monte Carlo method for Bayesian model updating

由 Lye, Adolphus, Marino, Luca
出版 2023

Comparison between Distance Functions for Approximate Bayesian Computation to Perform Stochastic Model Updating and Model Validation under Limited Data

由 Lye, Adolphus, Ferson, Scott, Xiao, Sicong
出版 2024

Stochastic Model Updating Using The Jenson-Shannon Divergence For Calibration and Validation Under Limited Data

由 Lye, Adolphus, Ferson, Scott, Xiao, Sicong
出版 2024

Towards a Robust Prediction of Material Properties by Artificial Intelligence and Probabilistic Methods

由 Lye, Adolphus, Nawal, Prinja, Patelli, Edoardo
出版 2023

On-line Bayesian Inference for Structural Health Monitoring under Model Uncertainty using Sequential Ensemble Monte Carlo

由 Lye, Adolphus, Cicirello, Alice, Patelli, Edoardo
出版 2023

COMPARISON BETWEEN DISTANCE FUNCTIONS FOR APPROXIMATE BAYESIAN COMPUTATION TOWARDS STOCHASTIC MODEL UPDATING AND MODEL VALIDATION UNDER LIMITED DATA

由 Lye, Adolphus, Ferson, Scott, Xiao, Sicong
出版 2023

An investigation towards the Uncertainty Model calibration approaches for NASA-Langley UQ Challenge 2019

由 Lye, Adolphus, Cicirello, Alice, Patelli, Edoardo
出版 2023

Data-driven material property prediction using probabilistic AI for Nuclear safety

由 Lye, Adolphus, Prinja, Nawal, Patelli, Edoardo
出版 2024

Sequential Ensemble Monte Carlo Sampler for On-Line Bayesian Inference of Time-Varying Parameter in Engineering Applications

由 Lye, Adolphus, Marino, Luca, Cicirello, Alice, Patelli, Edoardo
出版 2024

Data-driven Bayesian inference for stochastic model identification of nonlinear aeroelastic systems

由 McGurk, Michael, Lye, Adolphus, Renson, Ludovic, Yuan, Jie
出版 2024

Robust Probability Bounds Analysis for Failure Analysis under Lack of Data and Model Uncertainty

由 Lye, Adolphus, Gray, Ander, de Angelis, Marco, Ferson, Scott
出版 2023