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.

翻译：本文提出一种基于网络的优化方法EEE，旨在为预训练模型失效提供可验证的状态估计以修复其失效。为提升优化效率与收敛性——本研究中的核心指标——我们基于验证过程的误差采用三方面策略：首先，通过设计验证模块获取高维误差信息以提升误差的信息含量；其次，通过采用仅学习隐式误差的误差估计器集成，并利用约束集成探索收集高价值数据，降低误差传递的不确定性；最后，通过集成搜索确定最优状态以提升误差利用的有效性。在四个具有不同状态维度的实际工程问题上验证了所提框架的优势。结果表明，EEE在效率与收敛性方面与主流优化方法相当或更优。