8 citations · 17 across the 5 of their papers we have counts for
7 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…
Endeavouring Intelligent Process Self-Control by Employing Digital Twin Methodology: Proof-of-Concept Study for Cooking Applications
Maximilian Kannapinn, Michael Schäfer
This work demonstrates the use of the Digital Twin methodology to predict the water concentration and temperature of chicken meat. It marks a milestone on the path to autonomous co…
Autonomous Cooking with Digital Twin Methodology
Maximilian Kannapinn, Michael Schäfer
This work introduces the concept of an autonomous cooking process based on Digital Twin method- ology. It proposes a hybrid approach of physics-based full order simulations followe…
Physics-based Digital Twins for Autonomous Thermal Food Processing: Efficient, Non-intrusive Reduced-order Modeling
Maximilian Kannapinn, Minh Khang Pham, Michael Schäfer
One possible way of making thermal processing controllable is to gather real-time information on the product's current state. Often, sensory equipment cannot capture all relevant i…