1 citations · 1 across the 4 of their papers we have counts for
5 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…
Constitutive Kolmogorov-Arnold Networks (CKANs): Combining Accuracy and Interpretability in Data-Driven Material Modeling
Kian P. Abdolazizi, Roland C. Aydin, Christian J. Cyron +1
Hybrid constitutive modeling integrates two complementary approaches for describing and predicting a material's mechanical behavior: purely data-driven black-box methods and physic…
Viscoelastic Constitutive Artificial Neural Networks (vCANNs) a framework for data-driven anisotropic nonlinear finite viscoelasticity
Kian P. Abdolazizi, Kevin Linka, Christian J. Cyron
The constitutive behavior of polymeric materials is often modeled by finite linear viscoelastic (FLV) or quasi-linear viscoelastic (QLV) models. These popular models are simplifica…