PASL-U: PHYSICS-AWARE SOURCE RECOVERY WITH TEMPERATURE-PARAMETERIZED OPERATORS FOR GUIDED-WAVE DAMAGE LOCALIZATION

In guided-wave sensing, sensors record the source waveform that has been altered by propagation through the structure, so recovering the unknown source is an inverse problem that amounts to learning a mapping from measurements back to sources. However, the propagation operator changes with the environment: temperature changes the structure’s stiffness, density, and wave speed, and thus changes the source-to-sensor propagation characteristics. Conventional operator learning cannot separate environmental effects from source effects, and therefore attributes the former to the source. Outside the calibration range, the influence of the environment on propagation gradually increases, causing source recovery to degrade. We propose PASL-U (Physics-Anchored Operator Learning for Source Recovery under Environment Shifts), which preserves the environmental dependence of a physical propagation prior, learns a bounded correction from calibration environments, and explicitly predicts how this correction changes with the environment. The corrected operator is then frozen and used for source recovery in unseen environments. Experiments show that PASL-U maintains lower propagation and source errors under strict scalar and coupled-parameter extrapolation within the tested range. On measured guided waves, it also retains damage-recognition performance in temperature bands outside the propagation-calibration range.