2 citations · 5 across the 5 of their papers we have counts for
5 papers
PAINT: Parallel-in-time Neural Twins for Dynamical System Reconstruction
Andreas Radler, Vincent Seyfried, Johannes Brandstetter +1
Neural surrogates have shown great potential in simulating dynamical systems, while offering real-time capabilities. We envision Neural Twins as a progression of neural surrogates,…
Diverse Topology Optimization using Modulated Neural Fields
Andreas Radler, Eric Volkmann, Johannes Brandstetter +1
Topology optimization (TO) is a family of computational methods that derive near-optimal geometries from formal problem descriptions. Despite their success, established TO methods…
Simplified priors for Object-Centric Learning
Vihang Patil, Andreas Radler, Daniel Klotz +1
Humans excel at abstracting data and constructing \emph{reusable} concepts, a capability lacking in current continual learning systems. The field of object-centric learning address…
Geometry-Informed Neural Networks
Arturs Berzins, Andreas Radler, Eric Volkmann +3
Geometry is a ubiquitous tool in computer graphics, design, and engineering. However, the lack of large shape datasets limits the application of state-of-the-art supervised learnin…
A Dataset Perspective on Offline Reinforcement Learning
Kajetan Schweighofer, Andreas Radler, Marius-Constantin Dinu +5
The application of Reinforcement Learning (RL) in real world environments can be expensive or risky due to sub-optimal policies during training. In Offline RL, this problem is avoi…