Benchmarking data-driven material models on the classic Treloar dataset
Machine learning is rapidly reshaping constitutive modeling, offers new ways to learn material behavior directly from experimental data, and challenges long-established modeling paradigms. But with a growing number of machine-learning-based approaches available, how do they compare in practice? In this paper, we use the classic experimental data of Treloar to benchmark popular frameworks for hyperelasticity: (Generalized-Invariant) Constitutive Artificial Neural Networks, Physics-Augmented Neural Networks, (Adaptive) Material Fingerprinting, and Efficient Unsupervised Constitutive Law Identification & Discovery. We compare their fitting performance, computational cost, hyperparameter sens...
| Authors | Hagen Holthusen, Moritz Flaschel, Denisa Martonová et al. |
|---|---|
| Primary category | cs.AI |
| Published | 14 August 2026 |
| Last updated at source | 14 August 2026 |
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