9 citations · 28 across the 23 of their papers we have counts for
9 papers · 1 filter
Masked Conditioning for Deep Generative Models
Phillip Mueller, Jannik Wiese, Sebastian Mueller +1
Datasets in engineering domains are often small, sparsely labeled, and contain numerical as well as categorical conditions. Additionally. computational resources are typically limi…
Balanced Neural ODEs: nonlinear model order reduction and Koopman operator approximations
Julius Aka, Johannes Brunnemann, Jörg Eiden +2
Variational Autoencoders (VAEs) are a powerful framework for learning latent representations of reduced dimensionality, while Neural ODEs excel in learning transient system dynamic…
Exploring the Potentials and Challenges of Deep Generative Models in Product Design Conception
Phillip Mueller, Lars Mikelsons
The synthesis of product design concepts stands at the crux of early-phase development processes for technical products, traditionally posing an intricate interdisciplinary challen…
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…
Imitation Learning by State-Only Distribution Matching
Damian Boborzi, Christoph-Nikolas Straehle, Jens S. Buchner +1
Imitation Learning from observation describes policy learning in a similar way to human learning. An agent's policy is trained by observing an expert performing a task. While many…
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…