A variance-penalized Bayesian optimal experimental design method for nonlinear models uses prior-sampling Monte Carlo estimators and Bayesian optimization to identify robust designs with reduced utility variability.
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A neural model learns iterative refinement from noisy samples and spline inputs to find global minima, reporting 8.05% mean error on multi-modal tests versus 36.24% for spline initialization alone.
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Mean--Variance Risk-Aware Bayesian Optimal Experimental Design for Nonlinear Models
A variance-penalized Bayesian optimal experimental design method for nonlinear models uses prior-sampling Monte Carlo estimators and Bayesian optimization to identify robust designs with reduced utility variability.
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Neural Global Optimization via Iterative Refinement from Noisy Samples
A neural model learns iterative refinement from noisy samples and spline inputs to find global minima, reporting 8.05% mean error on multi-modal tests versus 36.24% for spline initialization alone.