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State-of-the-art and comparative review of adaptive sampling methods for kriging
Metamodels aim to approximate characteristics of functions or systems from the knowledge
extracted on only a finite number of samples. In recent years kriging has emerged as a …
extracted on only a finite number of samples. In recent years kriging has emerged as a …
Sparse polynomial chaos expansions: Literature survey and benchmark
Sparse polynomial chaos expansions (PCE) are a popular surrogate modelling method that
takes advantage of the properties of PCE, the sparsity-of-effects principle, and powerful …
takes advantage of the properties of PCE, the sparsity-of-effects principle, and powerful …
[HTML][HTML] Active learning for structural reliability: Survey, general framework and benchmark
Active learning methods have recently surged in the literature due to their ability to solve
complex structural reliability problems within an affordable computational cost. These …
complex structural reliability problems within an affordable computational cost. These …
Non-intrusive surrogate modeling for parametrized time-dependent partial differential equations using convolutional autoencoders
This paper presents a novel non-intrusive surrogate modeling scheme based on deep
learning for predictive modeling of complex systems, described by parametrized time …
learning for predictive modeling of complex systems, described by parametrized time …
Surrogate modelling for an aircraft dynamic landing loads simulation using an LSTM AutoEncoder-based dimensionality reduction approach
M Lazzara, M Chevalier, M Colombo, JG Garcia… - Aerospace Science and …, 2022 - Elsevier
Surrogate modelling can alleviate the computational burden of design activities as they rely
on multiple evaluations of high-fidelity models. However, the learning task can be adversely …
on multiple evaluations of high-fidelity models. However, the learning task can be adversely …
An efficient and versatile Kriging-based active learning method for structural reliability analysis
In structural reliability analysis, the development of an efficient and versatile active learning
method applicable to problems of varying complexities remains a challenging task. The …
method applicable to problems of varying complexities remains a challenging task. The …
Dimensionality reduction in surrogate modeling: A review of combined methods
Surrogate modeling has been popularized as an alternative to full-scale models in complex
engineering processes such as manufacturing and computer-assisted engineering. The …
engineering processes such as manufacturing and computer-assisted engineering. The …
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 …
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 …
Active learning with generalized sliced inverse regression for high-dimensional reliability analysis
It is computationally expensive to predict reliability using physical models at the design
stage if many random input variables exist. This work introduces a dimension reduction …
stage if many random input variables exist. This work introduces a dimension reduction …