78 citations · 97 across the 4 of their papers we have counts for
4 papers
Data-Driven Surrogate Modeling Techniques to Predict the Effective Contact Area of Rough Surface Contact Problems
Tarik Sahin, Jacopo Bonari, Sebastian Brandstaeter +1
The effective contact area in rough surface contact plays a critical role in multi-physics phenomena such as wear, sealing, and thermal or electrical conduction. Although accurate…
Physics-Informed Neural Networks for Solving Contact Problems in Three Dimensions
Tarik Sahin, Daniel Wolff, Alexander Popp
This paper explores the application of physics-informed neural networks (PINNs) to tackle forward problems in 3D contact mechanics, focusing on small deformation elasticity. We uti…
Towards a Hybrid Digital Twin: Physics-Informed Neural Networks as Surrogate Model of a Reinforced Concrete Beam
Tarik Sahin, Daniel Wolff, Max von Danwitz +1
In this study, we investigate the potential of fast-to-evaluate surrogate modeling techniques for developing a hybrid digital twin of a steel-reinforced concrete beam, serving as a…
Solving Forward and Inverse Problems of Contact Mechanics using Physics-Informed Neural Networks
T. Sahin, M. von Danwitz, A. Popp
This paper explores the ability of physics-informed neural networks (PINNs) to solve forward and inverse problems of contact mechanics for small deformation elasticity. We deploy P…