  A network-based optimization approach, EEE, is proposed for the purpose of
providing validation-viable state estimations to remediate the failure of
pretrained models. To improve optimization efficiency and convergence, the most
important metrics in the context of this research, we follow a three-faceted
approach based on the error from the validation process. Firstly, we improve
the information content of the error by designing a validation module to
acquire high-dimensional error information. Next, we reduce the uncertainty of
error transfer by employing an ensemble of error estimators, which only learn
implicit errors, and use Constrained Ensemble Exploration to collect high-value
data. Finally, the effectiveness of error utilization is improved by using
ensemble search to determine the most prosperous state. The benefits of the
proposed framework are demonstrated on four real-world engineering problems
with diverse state dimensions. It is shown that EEE is either as competitive or
outperforms popular optimization methods, in terms of efficiency and
convergence.
