Inverse Estimation of Thermophysical Properties in Semitransparent Materials via Particle Filter and Photothermal Radiometry

The characterization of photothermal properties in semi-transparent media is pivotal for advancing cutting-edge technologies; however, it is hindered by the complexity of internal photothermal processes. Conventional photothermal radiometry techniques often oversimplify internal radiation transport, resulting in substantial inaccuracies in media representation. To address this limitation, a coupled radiation-conduction modulated thermal wave model was developed in this work. Its integration with the Bayesian particle filter algorithm enabled high-precision robust simultaneous inversion of multiple critical thermophysical parameters, expanding the application boundaries of photothermal detection technology.