When deploying machine learning estimators in science and engineering (SAE) domains, it is critical to avoid failed estimations that can have disastrous consequences, e.g., in aero engine design. This work focuses on detecting and correcting failed state estimations before adopting them in SAE inverse problems, by utilizing simulations and performance metrics guided by physical laws. We suggest to flag a machine learning estimation when its physical model error exceeds a feasible threshold, and propose a novel approach, GEESE, to correct it through optimization, aiming at delivering both low error and high efficiency. The key designs of GEESE include (1) a hybrid surrogate error model to provide fast error estimations to reduce simulation cost and to enable gradient based backpropagation of error feedback, and (2) two generative models to approximate the probability distributions of the candidate states for simulating the exploitation and exploration behaviours. All three models are constructed as neural networks. GEESE is tested on three real-world SAE inverse problems and compared to a number of state-of-the-art optimization/search approaches. Results show that it fails the least number of times in terms of finding a feasible state correction, and requires physical evaluations less frequently in general.

翻译：在科学与工程领域部署机器学习估计器时，避免可能导致灾难性后果的失败估计至关重要（例如航空发动机设计）。本研究聚焦于在逆问题中采用机器学习状态估计前，通过利用物理学规律指导的仿真与性能指标，检测并修正失败的估计。我们建议在机器学习的物理模型误差超过可行阈值时标记该估计，并提出一种名为GEESE的新颖方法，通过优化实现低误差与高效率的双重目标。GEESE的关键设计包括：（1）混合代理误差模型，提供快速误差估计以降低仿真成本，并实现基于梯度的误差反馈反向传播；（2）两个生成模型，用于近似候选状态的概率分布，以模拟开发与探索行为。这三个模型均构建为神经网络。GEESE在三个真实科学与工程逆问题上进行测试，并与多种最先进的优化/搜索方法进行比较。结果表明，GEESE在寻找可行状态修正时失败次数最少，且通常需要更少的物理评估次数。