Machine learning of transient structural-thermal-optical-performance (STOP) models

Aleksandar Haber; John E. Draganov; Michael Krainak

doi:10.1117/12.2633338

3 October 2022 Machine learning of transient structural-thermal-optical-performance (STOP) models

Aleksandar Haber, John E. Draganov, Michael Krainak

Author Affiliations +

Proceedings Volume 12227, Applications of Machine Learning 2022; 1222708 (2022) https://doi.org/10.1117/12.2633338
Event: SPIE Optical Engineering + Applications, 2022, San Diego, California, United States

Abstract

In this paper, we investigate the feasibility of using machine learning methods for the estimation of StructuralThermal-Optical-Performance (STOP) models of reflective optics. We use a model of a Newtonian telescope system to test machine learning methods. To generate the estimation data, we model and simulate a transient finite-element STOP model of the Newtonian telescope by using COMSOL Multiphysics and LiveLink for MATLAB software module. We use a feedforward neural network structure to estimate the STOP model. The inputs and outputs of the neural network correspond to the inputs and outputs of a Vector AutoRegressive eXogenous (VARX) model. Our results show that large-scale STOP dynamics can be effectively approximated by a loworder neural network model. Consequently, low-order VARX or state-space models can be reconstructed from the parameters of the estimated feedforward neural network, and used for the prediction, state estimation, and design of model-based controllers. We use the TensorFlow and Keras machine learning libraries and Python to estimate the feedforward neural network model. The developed COMSOL, MATLAB, and Python codes are available online.

Conference Presentation

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Aleksandar Haber, John E. Draganov, and Michael Krainak "Machine learning of transient structural-thermal-optical-performance (STOP) models", Proc. SPIE 12227, Applications of Machine Learning 2022, 1222708 (3 October 2022); https://doi.org/10.1117/12.2633338

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