Tarik Sahin M.Sc.

Tarik Sahin M.Sc.

Academic Career

since 02/2021	Research and Teaching Associate, Institute for Mathematics and Computer-Based Simulation, University of the Bundeswehr Munich
10/2017-06/2020	Master of Science,  Computational Engineering, Master's thesis: Tetrahedral Mesh Refinement, a collaboration of MTU Aero Engines and the Institute of Structural Mechanics Ruhr University Bochum
09/2018-10/2019	Graduate Research Assistant, Institute of Lightweight, Steel and Composite Structures, Ruhr University Bochum
05/2018-10/2019	Graduate Research Assistant, Institute of Structural Mechanics, Ruhr University Bochum
10/2012-06/2017	Bachelor of Science, Aerospace Engineering, Middle East Technical University Bachelor's thesis: Design, Analysis, and Production of a Vertical Take-off and Landing Aircraft (VTOL)

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Research Focus

• Hybrid Digital Twins
• Physics-Informed Neural Networks (PINNs) for solving computational mechanics problems
• Combination of classical simulation methods with Machine Learning and Deep Learning (Data-driven simulations)
• Numerical methods, solvers, optimization

 

Use case 1: Hertz contact problem FEM vs PINNs

Use case 2: Prediction of beam deflection via PINNs with a wall scenario. 

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International Conference Contributions with Abstract

• Sahin, T., von Danwitz, M., Popp A.: Physics-Informed Neural Networks for Solid and Contact Mechanics, 92nd Annual Meeting of the International Association of Applied Mathematics and Mechanics, Aachen, Germany, August 15 - 19, 2022
• Sahin, T., Popp A.: Simulation-assisted deep learning approach for predicting the real contact area and the contact pressure, Mechanistic Machine Learning and Digital Twins for Computational Science, Engineering & Technology (MMLDT-CSET), San Diego, California, September 26 - 29, 2021

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Conference Proceedings and Book Contributions (with Peer-Review)

1. von Danwitz M, Kochmann T, Sahin T, Wimmer J, Braml T, Popp A (2023): Hybrid Digital Twins: A Proof of Concept for Reinforced Concrete Beams. Proceedings in Applied Mathematics and Mechanics, 22(1): e202200146, DOI (Open Access) 

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Teaching

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Courses & Exercises

• Modeling of Uncertainties and Data (FT 2021)
• Programming & Statistics (HT 2021, WT 2022)
• Modeling of Uncertainties and Data (FT 2022)

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Supervised Student Projects / Theses

1. Simulationsgestützte Machine Learning-Ansätze zur Vorhersage der maximalen Durchbiegung eines Trägers (2022)
2. Implementierung von Physik-Informierten Neuronalen Netzen in der Elastizitätstheorie unter Verwendung 2D-Rechengebieten (2022)
3. Optimierung der Hyperparameter für Physik-informierte Neuronale Netze (2023)