DEVELOPMENT OF A HYBRID INTELLIGENT SYSTEM ARCHITECTURE FOR AUTOMATED CONTROL OF THE NUMERICAL MODELING PROCESS

Abstract

The article presents the architecture of a hybrid intelligent system designed for automated control of the process of numerical modeling of optical properties of multilayer structures. The main goal of the development is to increase the accuracy, stability and computational efficiency in modeling complex physical phenomena in heterogeneous environments. The proposed system combines classical physical and mathematical models (in particular, RCWA) with adaptive grid algorithms, machine learning modules and optimization components based on gradient methods.The architecture is implemented as a modular system that includes a physical core, error estimation, optimization, machine learning modules, as well as a control agent that coordinates the operation of all subsystems. During the modeling, a dynamic change in the discretization parameters is provided depending on the local features of the spectrum – in particular, in the zones of spectral resonances, the grid is automatically condensed, while in stable areas – its rarefaction. This allows achieving high accuracy without excessive load on computing resources. The system also provides a posteriori assessment of the modeling accuracy, which allows to identify areas with potentially high error and adaptively refine the calculation parameters. The results of numerical experiments indicate a reduction in the average deviation from the reference solution to less than 1.2 % compared to more than 4.5 % in the case of using a non-adaptive scheme. A mechanism for generating recommendations in JSON format is also implemented, which suggests optimal geometric configurations of the multilayer structure to enhance the resonant behavior, reduce reflection and improve spectral selectivity. The hybrid system has demonstrated resistance to variations in input parameters and flexibility in application to new physical problems. The proposed architecture opens up prospects for its application in multiphysics problems, integration with cloud platforms and implementation of parallel computing. It can be used in spectroscopy, optical sensor analysis, design of photonic structures and study of thin-film materials. The conclusions drawn confirm the effectiveness of the synergy of classical numerical methods and modern intelligent technologies in high-precision modeling tasks.