大系数模型的图形凝缩

Pub Date : 2024-07-31 DOI:10.1134/S1064562424702090

B. N. Chetverushkin, V. A. Sudakov, Yu. P. Titov

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摘要

摘要利用机器学习模型和人工神经网络，开发了一种基于图浓缩的处理大型因素模型的独创方法。所提出的数学装置可用于规划和管理复杂的组织和技术系统，优化国家规模的大型社会经济对象，以及解决维护国民健康的问题（寻找药物的兼容性和优化医疗资源）。

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Graph Condensation for Large Factor Models

An original method for processing large factor models based on graph condensation using machine learning models and artificial neural networks is developed. The proposed mathematical apparatus can be used to plan and manage complex organizational and technical systems, to optimize large socioeconomic objects of national scale, and to solve problems of preserving the health of the nation (searching for compatibility of medications and optimizing health care resources).

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