Physics-Driven AI Correction in Laser Absorption Sensing Quantification

TL;DR

This paper introduces SPEC, a physics-driven AI framework for laser absorption sensing that enhances measurement reliability by detecting errors and iteratively correcting estimates, especially in out-of-distribution scenarios.

Contribution

The paper presents a novel hybrid framework combining ML estimation, physics-driven anomaly detection, and iterative correction with a surrogate error model for improved LAS quantification.

Findings

SPEC significantly improves estimation accuracy.

The correction mode outperforms existing optimization algorithms.

The framework is adaptable to various tasks without retraining.

Abstract

Laser absorption spectroscopy (LAS) quantification is a popular tool used in measuring temperature and concentration of gases. It has low error tolerance, whereas current ML-based solutions cannot guarantee their measure reliability. In this work, we propose a new framework, SPEC, to address this issue. In addition to the conventional ML estimator-based estimation mode, SPEC also includes a Physics-driven Anomaly Detection module (PAD) to assess the error of the estimation. And a Correction mode is designed to correct the unreliable estimation. The correction mode is a network-based optimization algorithm, which uses the guidance of error to iteratively correct the estimation. A hybrid surrogate error model is proposed to estimate the error distribution, which contains an ensemble of networks to simulate reconstruction error, and true feasible error computation. A greedy ensemble search is proposed to find the optimal correction robustly and efficiently from the gradient guidance of surrogate model. The proposed SPEC is validated on the test scenarios which are outside the training distribution. The results show that SPEC can significantly improve the estimation quality, and the correction mode outperforms current network-based optimization algorithms. In addition, SPEC has the reconfigurability, which can be easily adapted to different quantification tasks via changing PAD without retraining the ML estimator.