4 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…
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…
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…
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…