设计贝叶斯随机网络，对具有混合对流发热的威廉姆森流体拉伸流动模型进行数值处理

IF 1.7 4区工程技术 Q3 MECHANICS

Numerical Heat Transfer Part B-Fundamentals Pub Date : 2024-03-18 DOI:10.1080/10407790.2024.2329253

Zahoor Shah, Muhammad Asif Zahoor Raja, Muhammad Shoaib

引用次数: 0

摘要

本文通过贝叶斯正则化反向传播神经网络（BRB-NN）设计了一种随机网络范例，用于解释威廉姆森流体拉伸的动态。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Design of Bayesian stochastic networks for numerical treatment of Williamson fluid stretching flow model with mixed convected heat generation

In the presented article, a stochastic network paradigm through Bayesian Regularization backpropagation neural network (BRB-NN) is designed to interpret the dynamics of the Williamson fluid stretch...

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来源期刊

Numerical Heat Transfer Part B-Fundamentals 工程技术-力学

CiteScore

2.40

自引率

0.00%

发文量

审稿时长

2.8 months

期刊介绍： Published 12 times per year, Numerical Heat Transfer, Part B: Fundamentals addresses all aspects of the methodology for the numerical solution of problems in heat and mass transfer as well as fluid flow. The journal’s scope also encompasses modeling of complex physical phenomena that serves as a foundation for attaining numerical solutions, and includes numerical or experimental results that support methodology development. All submitted manuscripts are subject to initial appraisal by the Editor, and, if found suitable for further consideration, to peer review by independent, anonymous expert referees. The Editor reserves the right to reject without peer review any papers deemed unsuitable.