Beam pointing drift prediction in pulsed lasers by a probabilistic learning approach

In addition, beam-pointing shift also varies with laser sources as well as time, rendering the modeling of shifting errors difficult. While a few works have addressed the problem of predicting shift dynamics, several challenges still remain.

Specifically, a generic approach that can be easily applied to different laser systems is highly desired. In contrast to physical modeling approaches, we aim to predict beam-pointing drift using a well-established probabilistic learning approach, i.e., Gaussian Mixture Model (GMM). By exploiting sampled datapoints (collected from laser system) that comprise time and corresponding shifting errors, the joint distribution of time and shifting error can be estimated.