[HTML][HTML] Adaptive approaches in metamodel-based reliability analysis: A review
The present work reviews the implementation of adaptive metamodeling for reliability
analysis with emphasis in four main types of metamodels: response surfaces, polynomial …
analysis with emphasis in four main types of metamodels: response surfaces, polynomial …
Uncertainty quantification in machine learning for engineering design and health prognostics: A tutorial
On top of machine learning (ML) models, uncertainty quantification (UQ) functions as an
essential layer of safety assurance that could lead to more principled decision making by …
essential layer of safety assurance that could lead to more principled decision making by …
[图书][B] Uncertainty quantification: theory, implementation, and applications
RC Smith - 2024 - SIAM
Uncertainty quantification serves a central role for simulation-based analysis of physical,
engineering, and biological applications using mechanistic models. From a broad …
engineering, and biological applications using mechanistic models. From a broad …
Structure damage identification in dams using sparse polynomial chaos expansion combined with hybrid K-means clustering optimizer and genetic algorithm
Structural damage identification plays a crucial role in structural health monitoring. In this
study, a novelty method for structural damage identification is developed, which employs an …
study, a novelty method for structural damage identification is developed, which employs an …
Multi-parameter identification of concrete dam using polynomial chaos expansion and slime mould algorithm
This paper presents a novel methodology that combines polynomial chaos expansion and
slime mould algorithm for multi-parameter identification of concrete dams. This methodology …
slime mould algorithm for multi-parameter identification of concrete dams. This methodology …
Enhancing the explainability of regression-based polynomial chaos expansion by Shapley additive explanations
Surrogate models are indispensable tools in uncertainty quantification and global sensitivity
analysis. Polynomial chaos expansion (PCE) is one of the most widely used surrogate …
analysis. Polynomial chaos expansion (PCE) is one of the most widely used surrogate …
Metamodel-assisted hybrid optimization strategy for model updating using vibration response data
In this study, an effective and novel method, termed Metamodel Assisted Hybrid of Particle
Swarm Optimization with Genetic Algorithm (MA-HPSOGA), is developed to identify …
Swarm Optimization with Genetic Algorithm (MA-HPSOGA), is developed to identify …
Variance-based adaptive sequential sampling for polynomial chaos expansion
L Novák, M Vořechovský, V Sadílek… - Computer Methods in …, 2021 - Elsevier
This paper presents a novel adaptive sequential sampling method for building Polynomial
Chaos Expansion surrogate models. The technique enables one-by-one extension of an …
Chaos Expansion surrogate models. The technique enables one-by-one extension of an …
Efficient reliability analysis using prediction-oriented active sparse polynomial chaos expansion
In this paper, a prediction-oriented active sparse polynomial chaos expansion (PAS-PCE) is
proposed for reliability analysis. Instead of leveraging on additional techniques to reduce the …
proposed for reliability analysis. Instead of leveraging on additional techniques to reduce the …
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 …