Gürkan, Ceren

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https://hdl.handle.net/20.500.12469/6162
Name Variants
Gurkan, Ceren
Gurkan,Ceren
Gürkan,C.
Gurkan,C.
Gurkan C.
Ceren Gürkan
C. Gürkan
Ceren, Gurkan
Gürkan, Ceren
CEREN GÜRKAN
Gürkan, C.
G., Ceren
GÜRKAN, Ceren
Ceren GÜRKAN
Gürkan, CEREN
GÜRKAN, CEREN
G.,Ceren
Job Title
Dr. Öğr. Üyesi
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[email protected]
Main Affiliation
Civil Engineering
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Current Staff
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ORCID ID
0000-0002-1240-5801
Scopus Author ID
56709522500
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID
AAG-2043-2020

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Scholarly Output

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WoS Citation Count

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Scopus Citation Count

28

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Journal of Computational Physics1
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Now showing 1 - 6 of 6
  • Thumbnail Image
    Article
    Citation - WoS: 20
    Citation - Scopus: 23
    Stabilized Cut Discontinuous Galerkin Methods for Advection-Reaction Problems
    (Society for Industrial and Applied Mathematics Publications, 2020) Gürkan, Ceren; Sticko, Simon; Massing, André
    We develop novel stabilized cut discontinuous Galerkin methods for advection-reaction problems. The domain of interest is embedded into a structured, unfitted background mesh in \BbbR d where the domain boundary can cut through the mesh in an arbitrary fashion. To cope with robustness problems caused by small cut elements, we introduce ghost penalties in the vicinity of the embedded boundary to stabilize certain (semi-)norms associated with the advection and reaction operator. A few abstract assumptions on the ghost penalties are identified enabling us to derive geometrically robust and optimal a priori error and condition number estimates for the stationary advection-reaction problem which hold irrespective of the particular cut configuration. Possible realizations of suitable ghost penalties are discussed. The theoretical results are corroborated by a number of computational studies for various approximation orders and for two- and three-dimensional test problems.
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    Master Thesis
    EEG Verilerinden Gürültü Giderme
    (2024) Shahid, Tahura; Gürkan, Ceren
    Electroencephalography (EEG) is a vital tool for non-invasive brain activity monitoring, widely used in clinical and research settings, but often contaminated by noise from muscle movements, eye blinks, and electrical interference, which can obscure neural information. This thesis explores advanced machine learning techniques, focusing on autoencoders with Neural Ordinary Differential Equations (NODEs) and Residual Networks (ResNet), to enhance EEG denoising. While traditional methods like Independent Component Analysis (ICA) have been effective in separating EEG signals from artifacts by leveraging statistical independence, they struggle with the dynamic and nonlinear nature of EEG data. To overcome these limitations, this research integrates autoencoders with NODEs and ResNet, combining autoencoders' dimensionality reduction with NODEs' continuous-time dynamics and ResNet's skip connections to handle the complexity of multivariate EEG signals. The proposed hybrid framework significantly improves denoising accuracy, computational efficiency, and adaptability to different noise levels in bio-signals, outperforming traditional methods. Results, evaluated through metrics like Mean Squared Error (MSE), Relative Root Mean Squared Error (RRMSE), and correlation coefficients, show substantial improvements in noise removal for both synthetic and real EEG datasets, marking a significant advancement in EEG signal processing. Keywords: Electroencephalography (EEG), Denoising, Machine Learning, Independent Component Analysis (ICA), Neural Ordinary Differential Equations (ODEs), Residual Network, Autoencoders, Signal Processing, Brain Waves, Noise Removal
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    Article
    Extended Hybridizable Discontinuous Galerkin (x-Hdg) Method for Linear Convection-Diffusion Equations on Unfitted Domains
    (Academic Press inc Elsevier Science, 2024) Ahmad, Haroon; Gurkan, Ceren
    In this work, we propose a novel strategy for the numerical solution of linear convection diffusion equation (CDE) over unfitted domains. In the proposed numerical scheme, strategies from high order Hybridized Discontinuous Galerkin method and eXtended Finite Element method are combined with the level set definition of the boundaries. The proposed scheme and hence, is named as eXtended Hybridizable Discontinuous Galerkin (XHDG) method. In this regard, the Hybridizable Discontinuous Galerkin (HDG) method is eXtended to the unfitted domains; i.e., the computational mesh does not need to fit to the domain boundary; instead, the boundary is defined by a level set function and cuts through the background mesh arbitrarily. The original unknown structure of HDG and its hybrid nature ensuring the local conservation of fluxes is kept, while developing a modified bilinear form for the elements cut by the boundary. At every cut element, an auxiliary nodal trace variable on the boundary is introduced, which is eliminated afterwards while imposing the boundary conditions. Both stationary and time dependent CDEs are studied over a range of flow regimes from diffusion to convection dominated; using high order (p <= 4) XHDG through benchmark numerical examples over arbitrary unfitted domains. Results proved that XHDG inherits optimal (p + 1) and super (p + 2) convergence properties of HDG while removing the fitting mesh restriction.
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    Master Thesis
    Modelleme Nöronal Büyüme Dinamiklerinin Kullanılması Yapay Sinir Ağları
    (2024) Khan, Brıshna; Gürkan, Ceren
    Nöronlar ve sinir ağları üzerinde çalışarak, hesaplamalı sinirbilim ve yapay zeka teknikleri son birkaç on yılda beynin işleyişini anlama ve modelleme konusunda muazzam bir ilerleme kaydetmiştir. Bu çalışmada, yapay sinir ağları (ANNs) kullanarak, bilişsel aktivitelerde önemli rol oynayan iki temel sinir hücresi türü olan kortikal ve hipokampal nöronların büyüme desenlerini modelledim ve tahmin ettim. Bildiğimiz kadarıyla, daha önce hiçbir araştırmada nöron çoğalmasını tahmin etmek için sinir ağları kullanılmamıştır. Çalışmamız, nöron büyümesi tahmini için YSA tabanlı bir model oluşturarak bu bilgi açığını kapatmayı ve bu alanda yeni bilgiler eklemeyi amaçlamaktadır. Hipokampal ve kortikal nöronlar doğum sonrası (0-1. gün) fare beyinlerinden toplanmış ve 100 mm'lik bakteriyolojik sınıf bir petri kabında kültürlenmiştir. Hücreler 15 gün boyunca 37 °C'de inkübe edilmiş ve büyüme her altı saatte bir kontrol edilmiştir. Nöronal büyüme, Carl Zeiss Axiovert A1 invert floresan mikroskop kullanılarak izlenmiştir. Altı katmanlı bir Multi-Layer Perceptron (MLP) sinir ağı tasarlanmıştır. The Exponential Linear Unit (ELU) aktivasyon fonksiyonu, alfa değeri 1 ve öğrenme oranı 0.01 olacak şekilde kullanılmıştır. Bu ağ, günlük büyüme için 15 gün boyunca kortikal nöron verileri üzerinde eğitilmiş ve hipokampal nöron büyümesini tahmin etmek için test edilmiştir. Bu çalışmada hedef kortikal nöronun vücut büyümesi laboratuvarda gözlemlenmiştir. Her gün için nöronun alan değerleri elde edildikten sonra ANN modeli eğitilmiştir. Eğitimin ardından model hipokampal nöron üzerinde test edilmiştir. Yapının hassasiyetini doğrulamak için hipokampal nöron üzerindeki ANN tahmini, laboratuvardan elde edilen deneysel verilerle karşılaştırıldı ve çok yakın bir eşleşme bulundu. Bu çalışma, ANN'nın doğru modellendiği takdirde nöronların büyüme modelini tahmin edilebileceğini göstermiştir.
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    Article
    Citation - WoS: 1
    Citation - Scopus: 1
    An efficient PanAir integrated framework for automated analysis
    (Nature Portfolio, 2024) Shahid, Tahura; Gurkan, Ceren
    The work proposed here is an automated pre and post-processor integrated to PanAir that is is a high-order aerodynamic panel method-based software for flow analysis developed in 70s but still in active use especially for preliminary aircraft design. With the integrated environment proposed in this work, manipulation of input and output data to and from PanAir is bypassed successfully that is otherwise requires manual manipulations and use of third party software. The integrated environment is validated over a Cessna 210 aircraft with a modified NLF (1)-0414 airfoil. The flow around the aircraft is analyzed using PanAir together with the integrated environment and results show that pre and post processing times reduced and ease in PanAir use is increased significantly.
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    Article
    Citation - WoS: 2
    Citation - Scopus: 4
    Performance Analyses of Mesh-Based Local Finite Element Method and Meshless Global Rbf Collocation Method for Solving Poisson and Stokes Equations
    (Elsevier, 2022) Karakan, Ismet; Gurkan, Ceren; Avci, Cem
    Steady and unsteady Poisson and Stokes equations are solved using mesh dependent Finite Element Method and meshless Radial Basis Function Collocation Method to compare the performances of these two numerical techniques across several criteria. The accuracy of Radial Basis Function Collocation Method with multiquadrics is enhanced by implementing a shape parameter optimization algorithm. For the time-dependent problems, time discretization is done using Backward Euler Method. The performances are assessed over the accuracy, runtime, condition number, and ease of implementation. Three error kinds considered; least square error, root mean square error and maximum relative error. To calculate the least square error using meshless Radial Basis Function Collocation Method, a novel technique is implemented. Imaginary numerical solution surfaces are created, then the volume between those imaginary surfaces and the analytic solution surfaces is calculated, ensuring a fair error calculation. Lastly, all results are put together and trends are observed. The change in runtime vs. accuracy and number of nodes; and the change in accuracy vs. the number of nodes is analyzed. The study indicates the criteria under which Finite Element Method performs better and conditions when Radial Basis Function Collocation Method outperforms its mesh dependent counterpart.(c) 2022 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights reserved.