240 citations · 330 across the 5 of their papers we have counts for
6 papers
Uncertainty quantification in model discovery by distilling interpretable material constitutive models from Gaussian process posteriors
David Anton, Henning Wessels, Ulrich Römer +2
Constitutive model discovery refers to the task of identifying an appropriate model structure, usually from a predefined model library, while simultaneously inferring its material…
Deterministic and statistical calibration of constitutive models from full-field data with parametric physics-informed neural networks
David Anton, Jendrik-Alexander Tröger, Henning Wessels +3
The calibration of constitutive models from full-field data has recently gained increasing interest due to improvements in full-field measurement capabilities. In addition to the e…
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…
A deep learning driven pseudospectral PCE based FFT homogenization algorithm for complex microstructures
Alexander Henkes, Ismail Caylak, Rolf Mahnken
This work is directed to uncertainty quantification of homogenized effective properties for composite materials with complex, three dimensional microstructure. The uncertainties ar…
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…