Physics-Driven Anomaly Detection and Correction for Spectroscopic Parameter Estimation

Machine learning (ML) techniques are popular in many parameter estimation tasks; however, they face challenges in the real-world deployment due to the lack of robustness to errors. ML estimators are not able to ascertain performance in the presence of noise, variations in the data distribution, and anomalies in the test samples. This work proposes a novel framework, surrogate-based physical error correction (SPEC), which addresses the unmet need for measurement reliability estimation and self-correction under process data uncertainty, by bringing together physics-and network-based optimization. The workings of SPEC are demonstrated using the paradigm of gas parameter estimation in the laser absorption spectroscopy (LAS). It operates in two modes, estimation and correction. During estimation, SPEC provides an initial state estimate, with estimation reliability being assessed by the physics-driven anomaly detection (PAD) module, which uses a hybrid error, combining a nondifferentiable reconstruction error, calculated through an ensemble network, and a differentiable feasibility error. When an estimate is flagged as unreliable, the correction mode is enabled. This network-based optimization algorithm delivers efficient and robust state correction by using a greedy ensemble search. SPEC's performance is evaluated in a variety of experiments including outside-of-distribution and noisy data. Moreover, it offers reconfigurability through PAD configuration modification, eliminating the need for ML estimator retraining.