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Mustafa Serdar

As seen in: arXiv, Frontiers, MDPI
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Articles by Mustafa Serdar

Correction: Cleats on, battle real: sustainability and gender equality in women's football

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By Murat Yalcin, Mustafa Serdar, Eskisehir Technical
| Frontiers
A Correction on Cleats on, battle real: sustainability and gender equality in women's football by Ornek, S., Besiktas, M. Y., Terekli, M. S., Erkan, M., and Aksakal, N. (2026). Front. Psychol. 17:1743515.
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[2509.14437] Multi-Objective Loss Balancing in Physics-Informed Neural Networks for Fluid Flow Applications

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By Mustafa Serdar, Saiful Khan, Mustafa Serdar Çelebi
| arXiv
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[2505.18565] Learning Fluid-Structure Interaction Dynamics with Physics-Informed Neural Networks and Immersed Boundary Methods

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By Emre Cenk, Mustafa Serdar
| arXiv
[Submitted on 24 May 2025 ( v1 ), last revised 10 Sep 2025 (this version, v4)] Title:Learning Fluid-Structure Interaction Dynamics with Physics-Informed Neural Networks and Immersed Boundary Methods View a PDF of the paper titled Learning Fluid-Structure Interaction Dynamics with Physics-Informed Neural Networks and Immersed Boundary Methods, by Afrah Farea and 4 other authors View PDF HTML (experimental) Abstract:Physics-informed neural networks (PINNs) have emerged as a promising approach...
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QCPINN: Quantum-Classical Physics-Informed Neural Networks for Solving PDEs

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By Mustafa Serdar
| arXiv
[Submitted on 20 Mar 2025 ( v1 ), last revised 10 Apr 2025 (this version, v4)] Title:QCPINN: Quantum-Classical Physics-Informed Neural Networks for Solving PDEs View a PDF of the paper titled QCPINN: Quantum-Classical Physics-Informed Neural Networks for Solving PDEs, by Afrah Farea and 2 other authors View PDF HTML (experimental) Abstract:Physics-informed neural networks (PINNs) have emerged as promising methods for solving partial differential equations (PDEs) by embedding physical laws...
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[2411.15111] Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations

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By Mustafa Serdar
| arXiv
[Submitted on 22 Nov 2024] Title:Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations View a PDF of the paper titled Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations, by Afrah Fareaa and 1 other authors View PDF HTML (experimental) Abstract:We investigate the use of learnable activation functions in Physics-Informed Neural Networks (PINNs) for solving Partial Differential...
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Role of Partial Flexibility on Flow Evolution and Aerodynamic Power Efficiency over a Turbine Blade Airfoil

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By Kemal Koca, Mustafa Serdar Genç, Mustafa Serdar
| MDPI
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.
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Passive Flow Control Application using Single and Double Vortex Generator on S809 Wind Turbine Airfoil

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By Mustafa Özden, Mustafa Serdar Genç, Mustafa Serdar, Kemal Koca
| MDPI
1. Introduction Due to the consuming of fossil energy resources such as natural gas, coal and petroleum alternatives, as well as global warming, sustainable progress in the energy sector has significantly grown in the past decades. Regarding the low carbon economy, it has started to become more crucial, especially among developed countries. In recent years, wind energy has improved its reputation, and it has started to spearhead electricity production among renewables [1,2].
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Investigation of the Mechanical Behavior of a New Generation Wind Turbine Blade Technology

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By Mustafa Serdar Genç, Cihan Çiftci, Ayşe Erdoğan, Mustafa Serdar
| MDPI
Open AccessArticle by Cihan Çiftci 1,2, Ayşe Erdoğan 3 and Mustafa Serdar Genç 3,4,* 1 Department of Civil Engineering, Abdullah Gul University, Kayseri 38080, Turkey 2 Techno-CC R&D Innovation Ltd.
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