Surrogate models are popular tool to approximate the functional relationship of expensive simulation models in multiple scientific and engineering disciplines. Successful use of surrogate models can provide significant savings of computational cost. However, with a variety of surrogate model approaches available in literature, it is a difficult task to select an appropriate one at hand. In this paper, we present an overview of surrogate model approaches with an emphasis of their application for variance-based global sensitivity analysis, including polynomial regression model, high-dimensional model representation, state-dependent parameter, polynomial chaos expansion, Kriging/Gaussian Process, support vector regression, radial basis function, and low rank tensor approximation. The accuracy and efficiency of these approaches are compared with several benchmark examples. The strengths and weaknesses of these surrogate models are discussed, and the recommendations are provided for different types of applications. For ease of implementations, the packages, as well as toolboxes, of surrogate model techniques and their applications for global sensitivity analysis are collected.

Surrogate-assisted global sensitivity analysis: an overview

Computational simulations with different fidelities have been widely used in engineering design and optimization. A high-fidelity (HF) model is generally more accurate but also more time-consuming than the corresponding low-fidelity (LF) model. To take advantage of both HF and LF models, a number of multi-fidelity surrogate (MFS) models based on different surrogate models (e.g., Kriging, response surface, and radial basis function) have been developed, but MFS models based on support vector regression are rarely reported. In this paper, a new MFS model based on support vector regression, which is named Co_SVR, is developed. In the proposed method, the HF and LF samples are mapped into a high-dimensional feature space through a kernel function, and then, a linear model is utilized to evaluate the relationship between inputs and outputs. The root mean square error (RMSE) of HF responses of interest is used to express the training error of Co_SVR, and a heuristic algorithm, grey wolf optimizer, is used to obtain the optimal parameters. For verification, the Co_SVR model is compared with four popular multi-fidelity surrogate models and four single-fidelity surrogate models through a number of numerical cases and a pressure relief valve design problem. The results show that Co_SVR provides competitive performance in both numerical cases and the practical case. Moreover, the effects of key factors (i.e., the correlation between HF and LF models, the cost ratio of HF to LF models, and the combination of HF and LF samples) on the performance of Co_SVR are also explored.

A multi-fidelity surrogate model based on support vector regression

Efficiency is greatly concerned in reliability analysis community, especially for the problems with high-dimensional input random variables, because the computation cost of common reliability analysis methods may increase sharply with respect to the dimension of the problem. This paper proposes a novel meta-model based on the concepts of polynomial chaos expansion (PCE), dimension-reduction method (DRM), and information-theoretic entropy. Firstly, a PCE method based on DRM is developed to approximate the original function by a series of PCEs of univariate components. Compared with the PCE of the original function, the DRM-based PCE can reduce the computational cost. Before constructing the meta-model, a prior of the degree of the PCE is required, which determines the accuracy and efficiency of the PCE. However, the prior is usually determined by experience. According to the maximum entropy principle, this paper proposes an adaptive method for the selection of the polynomial chaos basis efficiently. With the adaptive PCE method based on DRM, a novel meta-model method is proposed, with which the reliability analysis can be achieved by Monte Carlo simulation efficiently. In order to verify the performance of the proposed method, three numerical examples and one structural dynamics engineering example are tested, with good accuracy and efficiency.

An adaptive polynomial chaos expansion for high-dimensional reliability analysis

An efficient multi-fidelity Kriging surrogate model-based method for global sensitivity analysis

Enhanced variable-fidelity surrogate-based optimization framework by Gaussian process regression and fuzzy clustering

Multi-level multi-fidelity sparse polynomial chaos expansion based on Gaussian process regression

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Ying Zhen

Author Tools

Chat about Author

Papers

Multi-level multi-fidelity sparse polynomial chaos expansion based on Gaussian process regression