Efficient variational Bayesian model updating by Bayesian active learning
As a main task of inverse problem, model updating has received more and more attention in
the area of inspection, sensing, and monitoring technologies during the recent decades …
the area of inspection, sensing, and monitoring technologies during the recent decades …
Bayesian probabilistic propagation of hybrid uncertainties: Estimation of response expectation function, its variable importance and bounds
Uncertainties existing in physical and engineering systems can be characterized by different
kinds of mathematical models according to their respective features. However, efficient …
kinds of mathematical models according to their respective features. However, efficient …
A sequential sampling-based Bayesian numerical method for reliability-based design optimization
For efficiently solving the Reliability-Based Design Optimization (RBDO) problem with multi-
modal, highly nonlinear and expensive-to-evaluate limit state functions (LSFs), a sequential …
modal, highly nonlinear and expensive-to-evaluate limit state functions (LSFs), a sequential …
Optimization of cavitation characteristics of aviation fuel centrifugal pump inducer based on surrogate model
JF Fu, XW Liu, JJ Yang, DW Yin, ZH Zhou - Structural and Multidisciplinary …, 2023 - Springer
This paper presents a method for optimizing the cavitation performance of aviation fuel
centrifugal pump inducer based on surrogate model with highly accurate simulations. To …
centrifugal pump inducer based on surrogate model with highly accurate simulations. To …
Combining Bayesian active learning and conditional Gaussian process simulation for propagating mixed uncertainties through expensive computer simulators
Resulted from the limited information on both parameters and excitation at the early design
stage of aerospace structures, evaluating the reliability with high accuracy has been …
stage of aerospace structures, evaluating the reliability with high accuracy has been …
Collaborative and adaptive Bayesian optimization for bounding variances and probabilities under hybrid uncertainties
Uncertainty quantification (UQ) has been widely recognized as of vital importance for
reliability-oriented analysis and design of engineering structures, and three groups of …
reliability-oriented analysis and design of engineering structures, and three groups of …
A probabilistic simulation method for sensitivity analysis of input epistemic uncertainties on failure probability
Estimating the failure probability is one of the core problems in reliability engineering.
However, the existence of epistemic uncertainties, which result from the incomplete …
However, the existence of epistemic uncertainties, which result from the incomplete …
A copula-based uncertainty propagation method for structures with correlated parametric p-boxes
H Liu, M Chen, C Du, J Tang, C Fu, G She - International Journal of …, 2021 - Elsevier
In the response analysis of uncertain structural models with limited information, probability-
boxes can be effectively employed to address the aleatory and epistemic uncertainty …
boxes can be effectively employed to address the aleatory and epistemic uncertainty …
A new Bayesian probabilistic integration framework for hybrid uncertainty propagation
F Liu, P He, Y Dai - Applied Mathematical Modelling, 2023 - Elsevier
Efficient propagation of uncertainty is one of the most critical tasks for uncertainty
quantification and reliable design in the presence of multi-source uncertainties. This work …
quantification and reliable design in the presence of multi-source uncertainties. This work …
Probability-oriented disturbance estimation-triggered control via collaborative and adaptive Bayesian optimization for reentry vehicles
The paper investigates the performance improvement issue for reentry vehicles under
uncertainties from the perspective of probability. The disturbance estimation-triggered …
uncertainties from the perspective of probability. The disturbance estimation-triggered …