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...

Deskribapen osoa

Xehetasun bibliografikoak

Egile Nagusiak:	Muhammad Ridho Alhafiz, Lavi Rizki Zuhal, Duong Viet Dung, Pramudita Satria Palar
Formatua:	Artigo
Hizkuntza:	Inglês
Argitaratua:	IEEE 2025-01-01
Saila:	IEEE Access
Gaiak:	Turbulence modeling neural network explainability Shapley additive explanations feature discovery
Sarrera elektronikoa:	https://ieeexplore.ieee.org/document/11136158/
Etiketak:	Etiketa erantsi Etiketarik gabe, Izan zaitez lehena erregistro honi etiketa jartzen!