Adjoint-compatible surrogates approximate the expected information gain for optimal experimental design in controlled ODE systems, remaining competitive in Gaussian regimes and beneficial for non-Gaussian priors.
Estimating Expected Information Gains for Experimental Designs with Application to the Random Fatigue-Limit Model
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Adjoint-Compatible Surrogates of the Expected Information Gain for Optimal Experimental Design
Adjoint-compatible surrogates approximate the expected information gain for optimal experimental design in controlled ODE systems, remaining competitive in Gaussian regimes and beneficial for non-Gaussian priors.