9 citations · 21 across the 5 of their papers we have counts for
5 papers
Learnable & Interpretable Model Combination in Dynamical Systems Modeling
Tobias Thummerer, Lars Mikelsons
During modeling of dynamical systems, often two or more model architectures are combined to obtain a more powerful or efficient model regarding a specific application area. This co…
Eigen-informed NeuralODEs: Dealing with stability and convergence issues of NeuralODEs
Tobias Thummerer, Lars Mikelsons
Using vanilla NeuralODEs to model large and/or complex systems often fails due two reasons: Stability and convergence. NeuralODEs are capable of describing stable as well as instab…
NeuralFMU: Presenting a workflow for integrating hybrid NeuralODEs into real world applications
Tobias Thummerer, Johannes Stoljar, Lars Mikelsons
The term NeuralODE describes the structural combination of an Artifical Neural Network (ANN) and a numerical solver for Ordinary Differential Equations (ODEs), the former acts as t…
Hybrid modeling of the human cardiovascular system using NeuralFMUs
Tobias Thummerer, Johannes Tintenherr, Lars Mikelsons
Hybrid modeling, the combination of first principle and machine learning models, is an emerging research field that gathers more and more attention. Even if hybrid models produce f…
NeuralFMU: Towards Structural Integration of FMUs into Neural Networks
Tobias Thummerer, Josef Kircher, Lars Mikelsons
This paper covers two major subjects: First, the presentation of a new open-source library called FMI.jl for integrating FMI into the Julia programming environment by providing the…