4 papers
Stable Port-Hamiltonian Neural Networks
Fabian J. Roth, Dominik K. Klein, Maximilian Kannapinn +2
In recent years, nonlinear dynamic system identification using artificial neural networks has garnered attention due to its broad potential applications across science and engineer…
Neural networks meet hyperelasticity: A monotonic approach
Dominik K. Klein, Mokarram Hossain, Konstantin Kikinov +3
We apply physics-augmented neural network (PANN) constitutive models to experimental uniaxial tensile data of rubber-like materials whose behavior depends on manufacturing paramete…
Digital twin inference from multi-physical simulation data of DED additive manufacturing processes with neural ODEs
Maximilian Kannapinn, Fabian Roth, Oliver Weeger
A digital twin is a virtual representation that accurately replicates its physical counterpart, fostering bi-directional real-time data exchange throughout the entire process lifec…
TwinLab: a framework for data-efficient training of non-intrusive reduced-order models for digital twins
Maximilian Kannapinn, Michael Schäfer, Oliver Weeger
Purpose: Simulation-based digital twins represent an effort to provide high-accuracy real-time insights into operational physical processes. However, the computation time of many m…