1 citations · 1 across the 2 of their papers we have counts for
3 papers
cs.LG2025★ 1 cited
Automating modeling in mechanics: LLMs as designers of physics-constrained neural networks for constitutive modeling of materials
Marius Tacke, Matthias Busch, Kian Abdolazizi +4
Large language model (LLM)-based agentic frameworks increasingly adopt the paradigm of dynamically generating task-specific agents. We suggest that not only agents but also special…
physics.comp-ph2025
Machine-learned domain partitioning for computationally efficient coupling of continuum and particle simulations of membrane fabrication
Matthias Busch, Gregor Häfner, Jiayu Xie +4
All simulation approaches eventually face limits in computational scalability when applied to large spatiotemporal domains. This challenge becomes especially apparent in molecular-…
cs.LG2024
Active partitioning: inverting the paradigm of active learning
Marius Tacke, Matthias Busch, Kevin Linka +2
Datasets often incorporate various functional patterns related to different aspects or regimes, which are typically not equally present throughout the dataset. We propose a novel,…