Abstract
Abstract
The possibility of using machine learning methods for solving the inverse problem of the laser-induced desorption quadrupole mass-spectrometry (LID-QMS) diagnostic is studied. The formulation of the problem is given, and a general scheme of its solution is proposed. A test model of gas transport in a solid body is considered, which is used to construct a database of gas transport parameters in the sample. The application of the synthetic data and machine learning methods, viz. the interpolation technique, the method of K nearest neighbors, and the neural networks, for solving the LID-QMS inverse problem is investigated. The advantages and disadvantages of each approach are discussed.
Funder
Ministry of Science and Higher Education of the Russian Federation
Subject
Condensed Matter Physics,Mathematical Physics,Atomic and Molecular Physics, and Optics