%0 Thesis %A RAJAN, ARVIND %D 2018 %T Moment-Based Uncertainty Propagation Using Multivariate Polynomials: Advances in Probabilistic Engineering Design %U https://bridges.monash.edu/articles/thesis/Moment-Based_Uncertainty_Propagation_Using_Multivariate_Polynomials_Advances_in_Probabilistic_Engineering_Design/6998936 %R 10.26180/5b8375b2794a9 %K Uncertainty %K Measurement %K Mellin Transform %K Polynomials %K Method of Moments %K Maximum Entropy Method %K Probability Density Function %K Reliability Analysis %K Robustness Analysis %K Reliability-Based Design Optimisation %K Reliability-Based Robust Design Optimisation %K Robust Design Optimisation %K Engineering Design %K Cumulative Distribution Function %K Random Variable %K Performance-Based Design %K Probability Theory %K Probability %K Statistics %K Manufacturing Safety and Quality %K Entropy %K Electrical and Electronic Engineering not elsewhere classified %K Optimisation %K Performance Evaluation; Testing and Simulation of Reliability %K Applied Statistics %K Design %X The research embarked on an adventurous journey five years ago to synthesise a novel and effective framework for the probabilistic analyses of engineering systems. The framework took the unpopular approach of using statistical moments to enable both better accuracy and much faster computation compared to the mainstream methods. Crucial theoretical developments in mathematical statistics became the key to unlock the full potential of the moment approach; which in turn, has led to the design of more economical yet safe real-world systems across multiple engineering disciplines, especially in the mission-critical projects where reliability and safety are the principal objectives. %I Monash University