1 citations · 1 across the 4 of their papers we have counts for
4 papers
LLM-driven design of physics-constrained constitutive models: two agents are better than one
Marius Tacke, Matthias Busch, Kian Abdolazizi +4
Developing constitutive models that capture how materials deform under load traditionally requires years of specialized expertise in continuum mechanics, machine learning, and scie…
Inelastic Constitutive Kolmogorov-Arnold Networks: A generalized framework for automated discovery of interpretable inelastic material models
Chenyi Ji, Kian P. Abdolazizi, Hagen Holthusen +2
A key problem of solid mechanics is the identification of the constitutive law of a material, that is, the relation between strain history and stress. Machine learning has lead to…
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…
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,…