АДАПТИВНИЙ ФРАКЦІЙНИЙ НЕЙРОННИЙ АЛГОРИТМ ДЛЯ МОДЕЛЮВАННЯ ТЕПЛОВОЛОГОПЕРЕНЕСЕННЯ

Ярослав Соколовський; Тетяна Самотій

Запропоновано фракційну нейронну мережу з адаптивним темпом навчання для моделювання динаміки неізотермічного тепло- та масоперенесення в капілярно-пористих матеріалах з урахуванням ефекту пам’яті та просторової нелокальності. Використано архітектуру нейронної мережі з роз’єднаною структурою, яка базується на функціях втрат, що враховують фізичні особливості досліджуваного процесу. Для навчання мережі використано поетапний підхід, що дозволяє зменшити чутливість до помилок та збоїв. Досліджено структуру мережі, оптимізовано її параметри, а також обрано відповідні активаційні функції та методи регуляризації з метою досягнення високої точності та достовірності результатів моделювання

дробові похідні

машинне навчання у фізичних процесах

моделювання нейронними мережами

адаптивний темп навчання

капілярно-пористі матеріали

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