240 citations · 476 across the 5 of their papers we have counts for
6 papers
Inferring Displacement Fields from Sparse Measurements Using the Statistical Finite Element Method
Vahab B. Narouie, Henning Wessels, Ulrich Römer
A well-established approach for inferring full displacement and stress fields from possibly sparse data is to calibrate the parameter of a given constitutive model using a Bayesian…
Physics-Informed Neural Networks for Material Model Calibration from Full-Field Displacement Data
David Anton, Henning Wessels
The identification of material parameters occurring in constitutive models has a wide range of applications in practice. One of these applications is the monitoring and assessment…
Spiking neural networks for nonlinear regression
Alexander Henkes, Jason K. Eshraghian, Henning Wessels
Spiking neural networks, also often referred to as the third generation of neural networks, carry the potential for a massive reduction in memory and energy consumption over tradit…
Three-dimensional microstructure generation using generative adversarial neural networks in the context of continuum micromechanics
Alexander Henkes, Henning Wessels
Multiscale simulations are demanding in terms of computational resources. In the context of continuum micromechanics, the multiscale problem arises from the need of inferring macro…
Physics informed neural networks for continuum micromechanics
Alexander Henkes, Henning Wessels, Rolf Mahnken
Recently, physics informed neural networks have successfully been applied to a broad variety of problems in applied mathematics and engineering. The principle idea is to use a neur…
The Neural Particle Method -- An Updated Lagrangian Physics Informed Neural Network for Computational Fluid Dynamics
Henning Wessels, Christian Weißenfels, Peter Wriggers
Numerical simulation is indispensable in industrial design processes. It can replace expensive experiments and even reduce the need for prototypes. While products designed with the…