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cs.LG2026
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…
cs.LG2025
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…