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Physics-Driven Anomaly Detection and Correction for Spectroscopic Parameter Estimation.

Ruiyuan Kang, Panos Liatsis

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    Summary
    This summary is machine-generated.

    This study introduces a new framework, surrogate-based physical error correction (SPEC), to improve machine learning reliability in parameter estimation. SPEC enhances accuracy by detecting and correcting errors in noisy or uncertain data, crucial for applications like laser absorption spectroscopy.

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    Area of Science:

    • Measurement science
    • Data science
    • Spectroscopy

    Background:

    • Machine learning (ML) estimators struggle with real-world data errors, including noise, distribution shifts, and anomalies.
    • Existing ML methods lack robust mechanisms for assessing and correcting performance under process data uncertainty.

    Purpose of the Study:

    • To introduce a novel framework, surrogate-based physical error correction (SPEC), for reliable measurement estimation and self-correction.
    • To address the limitations of ML techniques in handling data uncertainty and errors.

    Main Methods:

    • SPEC integrates physics- and network-based optimization for estimation and correction.
    • A physics-driven anomaly detection (PAD) module assesses estimation reliability using hybrid errors (reconstruction and feasibility).
    • A greedy ensemble search enables robust state correction when estimates are unreliable.

    Main Results:

    • SPEC demonstrates robust performance in gas parameter estimation within laser absorption spectroscopy (LAS).
    • The framework effectively handles outside-of-distribution and noisy data scenarios.
    • SPEC offers reconfigurability via PAD configuration, avoiding ML estimator retraining.

    Conclusions:

    • SPEC provides a reliable method for measurement estimation and self-correction under data uncertainty.
    • The proposed framework enhances the robustness and applicability of ML techniques in real-world applications.
    • SPEC's hybrid approach offers a significant advancement in reliable parameter estimation.