78 citations · 99 across the 16 of their papers we have counts for
5 papers · 1 filter
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…
Sequential drone routing for data assimilation on a 2D airborne contaminant dispersion problem
Daniele Giovanni Gioia, Jacopo Bonari, Daniel Lichte +1
The combined use of data from different sources can be critical in emergencies, where accurate models are needed to make real-time decisions, but high-fidelity representations and…
Contaminant Dispersion Simulation in a Digital Twin Framework for Critical Infrastructure Protection
Max von Danwitz, Jacopo Bonari, Philip Franz +3
A digital twin framework for rapid predictions of atmospheric contaminant dispersion is developed to support informed decision making in emergency situations. In an offline prepara…
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…
Towards Real-Time Urban Physics Simulations with Digital Twins
Jacopo Bonari, Lisa Kühn, Max von Danwitz +1
Urban populations continue to grow, highlighting the critical need to safeguard civilians against potential disruptions, such as dangerous gas contaminant dispersion. The digital t…