Presents a diversity-aware batch-mode query-by-committee active learning method using cosine similarity to select non-redundant queries for efficient stress-space sampling in data-driven constitutive modeling.
Component-based machine learning paradigm for discovering rate-dependent and pressure-sensitive level-set plasticity models
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Diversity-Aware Batch-Mode Active Learning for Efficient Sampling in Data-Driven Constitutive Modeling
Presents a diversity-aware batch-mode query-by-committee active learning method using cosine similarity to select non-redundant queries for efficient stress-space sampling in data-driven constitutive modeling.