3 papers
cs.AI2026
Benchmarking data-driven material models on the classic Treloar dataset
Hagen Holthusen, Moritz Flaschel, Denisa Martonová +1
Machine learning is rapidly reshaping constitutive modeling, offers new ways to learn material behavior directly from experimental data, and challenges long-established modeling pa…
cond-mat.soft2025
Generalized invariants meet constitutive neural networks: A novel framework for hyperelastic materials
Denisa Martonová, Alain Goriely, Ellen Kuhl
The major challenge in determining a hyperelastic model for a given material is the choice of invariants and the selection how the strain energy function depends functionally on th…
cs.CE2025
Material Fingerprinting: A shortcut to material model discovery without solving optimization problems
Moritz Flaschel, Denisa Martonová, Carina Veil +1
We propose Material Fingerprinting, a new method for the rapid discovery of mechanical material models from direct or indirect data that avoids solving potentially non-convex optim…