Pareto-driven gradient-boosted Bayesian optimization for multi-objective inverse design of Aerogel-cement-EPS panels in small-sample regimes

Pareto-driven gradient-boosted Bayesian optimization for multi-objective inverse design of Aerogel-cement-EPS panels in small-sample regimes

This study addresses the challenges of escaping local optima in traditional engineering design methods and data scarcity during research and development. It proposes a novel multi-objective optimization approach integrating data augmentation, data expansion, gradient-boosted Bayesian optimization, a...

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Bibliographic Details
Main Authors: Lu Lu, Anming Bai, Haodong Wang, Wenjia Xi, Shan Yun, Shangbing Gao, Mingming Wang, Xinguo Sun
Format: Article
Language:English
Published: Elsevier 2025-12-01
Series:Case Studies in Construction Materials
Subjects:
Aerogel-cement-EPS composites
Bayesian optimization
Small-sample learning
Multi-objective optimization
Inverse design
Online Access:http://www.sciencedirect.com/science/article/pii/S2214509525009441
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