Search Results for author:
Found 7 papers, 3 papers with code
Analyzing the coupling process of distributed mixed real-virtual prototypes
This contribution introduces a novel, adaptive method to compensate for constant time-delays in potentially highly nonlinear, spatially distributed mixed real-virtual prototypes, using small feedforward neural networks.
Eigen-informed NeuralODEs: Dealing with stability and convergence issues of NeuralODEs
Because of the vanishing gradient at a local minimum, the NeuralODE is often not capable of leaving it and converge to the right solution.
NeuralFMU: Presenting a workflow for integrating hybrid NeuralODEs into real world applications
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 the right-hand side of the ODE to be solved.
Imitation Learning by State-Only Distribution Matching
We propose a non-adversarial learning-from-observations approach, together with an interpretable convergence and performance metric.
State-Only Imitation Learning by Trajectory Distribution Matching
Our training objective minimizes the Kulback-Leibler divergence between the policy and expert state transition trajectories which can be optimized in a non-adversarial fashion.
Hybrid modeling of the human cardiovascular system using NeuralFMUs
Hybrid modeling, the combination of first principle and machine learning models, is an emerging research field that gathers more and more attention.
NeuralFMU: Towards Structural Integration of FMUs into Neural Networks
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 possibility to load, parameterize and simulate FMUs.
