Uncertainty quantification in tree structure and polynomial regression algorithms toward material indices prediction

Uncertainty quantification in tree structure and polynomial regression algorithms toward material indices prediction

Machine learning’s integration into reliability analysis holds substantial potential to ensure infrastructure safety. Despite the merits of flexible tree structure and formulable expression, random forest (RF) and evolutionary polynomial regression (EPR) cannot contribute to reliability-based design...

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Bibliographic Details
Main Authors: Geng-Fu He, Pin Zhang, Zhen-Yu Yin
Format: Article
Language:English
Published: Cambridge University Press 2025-01-01
Series:Data-Centric Engineering
Subjects:
evolutionary polynomial regression
quantile
random forest
uncertainty quantification
variational inference
Online Access:https://www.cambridge.org/core/product/identifier/S263267362500005X/type/journal_article
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