Physics-Driven ML-Based Modelling for Correcting Inverse Estimation
Main Conference Track
Authors: ruiyuan kang, Tingting Mu, Panagiotis Liatsis, Dimitrios Kyritsis

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.