Global sensitivity analysis for multivariate outputs using generalized RBF-PCE metamodel enhanced by variance-based sequential sampling

Chen, Lin; Huang, Hanyan

doi:10.1016/j.apm.2023.10.047

Cited by 7 publications

(1 citation statement)

References 56 publications

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“…By employing a chaotic perturbation operator, GSA addresses these issues effectively, which has been utilized for parameter optimization in machine learning, as cited in [23]. To ensure that the FSI prediction model achieves higher predictive accuracy and generalizability, this study employs GSA to optimize three critical parameters in the RBFNN [59]: the number of nodes in the hidden layer C j , the spread value σ j , and the network connection weights ω j . The optimization process ensures the reasonableness of the parameters and enhances the robustness of the predictions.…”

Section: Lagrange's Ahm-critic Coefficient Couplingmentioning

confidence: 99%

Systemic Financial Risk Forecasting: A Novel Approach with IGSA-RBFNN

Tian,

2024

Mathematics

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Accurate measurement of systemic financial risk is crucial for maintaining the stability of financial markets. Taking China as the subject of investigation, the Chinese Financial Stress Index (CFSI) indicator system was constructed by integrating six dimensions and employing Gray Relation Analysis (GRA) to reduce the dimensionality of the indicators. The CFSI was derived using the Attribute Hierarchy Model (AHM) method with the Criteria Importance Through the Intercriteria Correlation (CRITIC) method, and an Improved Gravitational Search Algorithm (IGSA)-optimized Radial Basis Function Neural Network (RBFNN) was proposed for out-of-sample prediction of CFSI trends from 2024 to 2026. By analyzing the trends in financial pressure indicators, the intricate relationship between financial pressure and economic activity can be effectively discerned. The research findings indicate that (1) the CFSI is capable of accurately reflecting the current financial stress situation in China, and (2) the IGSA-RBFNN demonstrates strong robustness and generalization capabilities, predicting that the CFSI index will reach a peak value of 0.543 by the end of 2024, and there exists a regular pattern of stress rebound towards the end of each year. The novel methodology enables policymakers and regulatory authorities to proactively identify potential risks and vulnerabilities, facilitating the formulation of preventive measures.

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Section: Lagrange's Ahm-critic Coefficient Couplingmentioning

confidence: 99%

Systemic Financial Risk Forecasting: A Novel Approach with IGSA-RBFNN

Tian,

2024

Mathematics

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Digital Methods for the Fatigue Assessment of Engineering Steels

Fliegener,

Rosenberger,

Luke

et al. 2024

Adv Eng Mater

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Engineering steels are used for a wide range of applications in which their fatigue behavior is a crucial design factor. The fatigue properties depend on various influencing factors such as chemical composition, heat treatment, surface properties, load parameters, microstructure, and others. During product development, various material characterization and qualification experiments are mandatory. For a faster and more cost‐efficient development, data driven methods (machine learning) promise to replace or to complement material testing by prediction of the fatigue strength. With an ontology‐based, semantically‐linked knowledge graph, representing the manufacturing history of the material, the influence of the parameters of the process chain on the resulting properties can be accounted for. Herein, it is shown how a fatigue database containing a wide range of materials is assembled from literature. After postprocessing and curation of the data, machine learning predictions of mechanical properties are discussed under multiple aspects. A domain ontology is defined, containing the relevant class definitions for the use case. After applying a data integration and mapping workflow, it is shown how the data can be systematically queried using knowledge graphs describing the manufacturing history of the materials.

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