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Physics-InformedNeural Networks inPolymers: A Review :научное издание Malashin IvanTynchenko VadimGantimurov Andrei2025 год

Physics-Informed Neural Networks in Polymers: A Review : научное издание
статья из журнала

Дополнительные ссылки: eLibrary
База данных: Каталог библиотеки СФУ
Библиографическое описание: Physics-Informed Neural Networks in Polymers: A Review : научное издание / I. Malashin, V. Tynchenko, A. Gantimurov [et al.]. - Текст : непосредственный // Polymers. - 2025. - Т. 17, № 8. - P. 1108. - ISSN 2073-4360, DOI 10.3390/polym17081108.
Аннотация: <jats:p>The modeling and simulation of polymer systems present unique challenges due to their intrinsic complexity and multi-scale behavior. Traditional computational methods, while effective, often struggle to balance accuracy with computational efficiency, especially when bridging the atomistic to macroscopic scales. Recently, physics-informed neural networks (PINNs) have emerged as a promising tool that integrates data-driven learning with the governing physical laws of the system. This review discusses the development and application of PINNs in the context of polymer science. It summarizes the recent advances, outlines the key methodologies, and analyzes the benefits and limitations of using PINNs for polymer property prediction, structural design, and process optimization. Finally, it identifies the current challenges and future research directions to further leverage PINNs for advanced polymer modeling.</jats:p>
Год издания: 2025
Авторы: Malashin Ivan , Tynchenko Vadim , Gantimurov Andrei , Nelyub Vladimir , Borodulin Aleksei
Издательство: MDPI, Basel
Источник: Polymers
Выпуск: Т. 17, № 8
Номера страниц: 1108
Держатель оригинала документа: Artificial Intelligence Technology Scientific and Education Center, Bauman Moscow State Technical University, 105005 Moscow, Russia, Scientific Department, Far Eastern Federal University, 690922 Vladivostok, Russia
Количество экземпляров:
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