A Bayesian reinforcement learning reliability method that combines Bayesian inference for
the failure probability estimation and reinforcement learning-guided sequential experimental …

This paper proposes a novel method based on arbitrary Polynomial Chaos (aPC) to
evaluate how parameter and variable uncertainty impacts on the dynamic response of …

Multifidelity uncertainty quantification (MF UQ) sampling approaches have been shown to
significantly reduce the variance of statistical estimators while preserving the bias of the …

To alleviate the intensive computational burden of reliability analysis, a new parallel active
learning reliability method is proposed from the multi-point look-ahead paradigm. First, in the …

Prediction of thermal crack propagation in desiccated soils is imperfect due to the obscure
field measurements, modeling approximations, and the underlying soil uncertainties. To …

Uncertainty quantification (UQ) and inference involving a large number of parameters are
valuable tools for problems associated with heterogeneous and non-stationary behaviors …

This work introduces an efficient methodology for:(i) predicting dynamic responses across a
broad frequency band for large-scale, highly complex structures, and (ii) forecasting their …

Precise determination of thermal diffusivity at high temperatures is crucial for aerospace and
energy industries. Periodic heating techniques such as Å ngström's method are data-rich …

Quantitative predictions of scalar transport in natural porous media is a nontrivial task given
the presence of multi-scale spatial heterogeneity in the permeability field. Due to data …

Full lifecycle high fidelity digital twin is a complex model set contains multiple functions with
high dimensions and multiple variables. Quantifying uncertainty for such complex models …