A multivariate active learning approach for polynomial chaos expansion selects samples by aggregated output variance to improve surrogate accuracy and stability for vector-valued engineering responses.
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A multi-agent system combining contextual bandits, LLM agents, and semantic checkpoints improves convergence and robustness in adaptive method selection for sensitivity analysis and uncertainty quantification.
A probabilistic framework combining robust regression, Sobol indices, Monte Carlo propagation, and AIC/BIC model selection for uncertainty-aware creep remaining useful life prediction.
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Learning to Choose: An Empowerment-Guided Multi-Agent System with semantic communication for Adaptive Method Selection
A multi-agent system combining contextual bandits, LLM agents, and semantic checkpoints improves convergence and robustness in adaptive method selection for sensitivity analysis and uncertainty quantification.