OPTIMAL EXPERIMENTAL DESIGN BASED ON MUTUAL INFORMATION AND SUBMODULARITY

A computer-implemented method is presented for optimal experimental design to correct a misspecified model approximating a behavior of a dynamic system. The method includes formulating an experiment, determining experimental settings for configuring controllable experimental parameters based on mutual information and submodularity, measuring informative values associated with each choice of experimental design, as prescribed by the controllable experimental parameters, and learning a correction function based on the measured informative values. The computer-implemented method further includes determining an experimental design setup for gaining information content, and combining the experimental design setup with the experimental settings to construct a corrected model of the dynamic system.