An Explainable Deep Learning for Data-Driven Turbulence Model Feature Discovery

This work presents an explainable deep learning for turbulence modelling feature discovery in several flow configurations where conventional approaches perform poorly. The proposed neural network-based turbulence model predicts the Reynolds stress based on the mean flow quantities. Furthermore, the...

Celý popis

Podrobná bibliografie

Hlavní autoři:	Muhammad Ridho Alhafiz, Lavi Rizki Zuhal, Duong Viet Dung, Pramudita Satria Palar
Médium:	Artigo
Jazyk:	Inglês
Vydáno:	IEEE 2025-01-01
Edice:	IEEE Access
Témata:	Turbulence modeling neural network explainability Shapley additive explanations feature discovery
On-line přístup:	https://ieeexplore.ieee.org/document/11136158/
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!