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Physically meaningful parameter identification for complex material constitutive models using feature-guided surrogate-based optimization
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DOI：10.1016/j.tws.2026.114905.png)
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Abstract
En 中文
• Enhanced physical interpretability and reduced non-uniqueness. • Enhanced identification efficiency and stability via feature-guided surrogates. • Balanced global exploration and local feature refinement for identification.
Keywords:
parameter identification
constitutive models
surrogate-based optimization
physical interpretability
feature-guided approach
Journal
T
IF:
6.6
Papers:
1.1W
Citations:
4.0W