基于物理驱动的机器学习建模用于修正逆估计
Physics-Driven ML-Based Modelling for Correcting Inverse Estimation
AI总结：
本文提出GEESE方法，通过混合代理误差模型和生成模型检测并修正科学与工程逆问题中的失败机器学习状态估计，在三个真实问题上以最少失败次数和更低评估成本实现低误差修正。
摘要：
在科学与工程（SAE）领域部署机器学习估计器时，避免可能带来灾难性后果的失败估计至关重要，例如在航空发动机设计中。本工作聚焦于在SAE逆问题中采用状态估计之前，利用受物理定律指导的仿真和性能指标来检测并修正失败的状态估计。我们建议当机器学习估计的物理模型误差超过可行阈值时对其进行标记，并提出一种新方法GEESE，通过优化来修正该估计，旨在同时实现低误差和高效率。GEESE的关键设计包括：（1）一个混合代理误差模型，用于提供快速误差估计以降低仿真成本，并支持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，以模拟利用和探索行为。这三个模型均构建为神经网络。GEESE在三个真实世界的SAE逆问题上进行了测试，并与多种最先进的优化/搜索方法进行了比较。结果表明，在找到可行状态修正方面，GEESE失败的次数最少，并且通常需要更少的物理评估次数。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
