RAMT

Авторы

A.Yu. Kokhanovskiy ¹ , L. M. Dorogin ^2, ³ , X.A. Egorova ⁴ , Е.В. Антонов ² , D.A. Sinev ⁴

¹ Физический факультет, Университет ИТМО, Кронверкский пр., 49, лит. A, Санкт-Петербург, 197101, Россия

² Институт перспективных систем передачи данных Университета ИТМО, Кронверкский пр., 49, лит. А, 197101, Санкт-Петербург, Россия

³ Department of Molecules and Materials, University of Twente, Enschede, Netherlands

⁴ Институт лазерных технологий, Университет ИТМО, Кронверкский пр., 49, лит. A, Санкт-Петербург, 197101, Россия

10.17586/2687-0568-2025-7-2-88-104

Rev. Adv. Mater. Technol., 2025, vol. 7, no. 2, pp. 88–104

Аннотация

Представлен краткий обзор последних достижений в области нейронных сетей, основанных на физике (PINN), для приложений, связанных с трибологией. Трибологические приложения рассматриваются как фундаментально зависящие от множества мультифизических явлений, которые необходимо учитывать при создании PINN. Мультифизические входные данные для PINNs могут включать данные о материалах, топографии и шероховатости поверхности, а также данные аналитической трибометрии, которые используются для анализа трения, смазки, износа, смачивания, теплопередачи, структурных и фазовых переходов, химических реакций, растрескивания и фреттинга. Создание мульти-PINN, которые соединяют отдельные трибологические явления, представляет собой практически важную и сложную проблему, которую еще предстоит решить.

Ключевые слова

трибология; трение; нейронные сети; мультифизика; машинное обучение

Финансирование

ITMO University Research Projects in AI Initiative (RPAII): 640114

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Развитие и перспективы физически информированных нейронных сетей для трибологических применений с мультифизической интеграцией

Авторы

Аннотация

Ключевые слова

Финансирование