Monte Carlo and variance reduction methods for structural reliability analysis: A comprehensive review
C Song, R Kawai - Probabilistic Engineering Mechanics, 2023 - Elsevier
Monte Carlo methods have attracted constant and even increasing attention in structural
reliability analysis with a wide variety of developments seamlessly presented over decades …
reliability analysis with a wide variety of developments seamlessly presented over decades …
On the influence of over-parameterization in manifold based surrogates and deep neural operators
Constructing accurate and generalizable approximators (surrogate models) for complex
physico-chemical processes exhibiting highly non-smooth dynamics is challenging. The …
physico-chemical processes exhibiting highly non-smooth dynamics is challenging. The …
An unsupervised learning approach for wayside train wheel flat detection
One of the most common types of wheel damage is flats which can cause high maintenance
costs and enhance the probability of failure and damage to the track components. This study …
costs and enhance the probability of failure and damage to the track components. This study …
An overview on uncertainty quantification and probabilistic learning on manifolds in multiscale mechanics of materials
C Soize - Mathematics and Mechanics of Complex Systems, 2023 - msp.org
An overview of the author's works, many of which were carried out in collaboration, is
presented. The first part concerns the quantification of uncertainties for complex engineering …
presented. The first part concerns the quantification of uncertainties for complex engineering …
A survey of unsupervised learning methods for high-dimensional uncertainty quantification in black-box-type problems
Constructing surrogate models for uncertainty quantification (UQ) on complex partial
differential equations (PDEs) having inherently high-dimensional O (10 n), n≥ 2, stochastic …
differential equations (PDEs) having inherently high-dimensional O (10 n), n≥ 2, stochastic …
Detection of wheel polygonization based on wayside monitoring and artificial intelligence
This research presents an approach based on artificial intelligence techniques for wheel
polygonization detection. The proposed methodology is tested with dynamic responses …
polygonization detection. The proposed methodology is tested with dynamic responses …
Probabilistic-learning-based stochastic surrogate model from small incomplete datasets for nonlinear dynamical systems
We consider a high-dimensional nonlinear computational model of a dynamical system,
parameterized by a vector-valued control parameter, in the presence of uncertainties …
parameterized by a vector-valued control parameter, in the presence of uncertainties …
Learning in latent spaces improves the predictive accuracy of deep neural operators
Operator regression provides a powerful means of constructing discretization-invariant
emulators for partial-differential equations (PDEs) describing physical systems. Neural …
emulators for partial-differential equations (PDEs) describing physical systems. Neural …
Early identification of unbalanced freight traffic loads based on wayside monitoring and artificial intelligence
The identification of instability problems in freight trains circulation such as unbalanced
loads is of particular importance for railways management companies and operators. The …
loads is of particular importance for railways management companies and operators. The …
Machine learning accelerated transient analysis of stochastic nonlinear structures
This paper presents a non-intrusive surrogate modeling scheme for transient response
analysis of nonlinear structures involving random parameters. The proposed scheme utilizes …
analysis of nonlinear structures involving random parameters. The proposed scheme utilizes …