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...

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Bibliografiske detaljer

Principais autores:	Muhammad Ridho Alhafiz, Lavi Rizki Zuhal, Duong Viet Dung, Pramudita Satria Palar
Format:	Artigo
Sprog:	Inglês
Udgivet:	IEEE 2025-01-01
Serier:	IEEE Access
Fag:	Turbulence modeling neural network explainability Shapley additive explanations feature discovery
Online adgang:	https://ieeexplore.ieee.org/document/11136158/
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