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Benchmarking data-driven material models on the classic Treloar dataset — arXiv:2608.14063

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.14063
License (as archived): CC0-1.0
Archived: 2026-10-19T02:20:31.830Z
SHA-256 of the archived text: a5bbf5b50546df7e2b1d5c0d1f413b047a8c540b526ae9939a0c4c4f9f813766
Stable URL of this page: https://metraipolis.com/documento/dc_bf31324ccd41

How to cite

Benchmarking data-driven material models on the classic Treloar dataset — arXiv:2608.14063 — arXiv (en), https://arxiv.org/abs/2608.14063 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_bf31324ccd41 (Metraipolis archive dc_bf31324ccd41, sha256 a5bbf5b50546df7e2b1d5c0d1f413b047a8c540b526ae9939a0c4c4f9f813766)

BibTeX

@misc{dcbf31324ccd41,
  title = {Benchmarking data-driven material models on the classic Treloar dataset — arXiv:2608.14063},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.14063},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_bf31324ccd41; sha256 a5bbf5b50546df7e2b1d5c0d1f413b047a8c540b526ae9939a0c4c4f9f813766},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.14063. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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 sensitivity, and ease of implementation. Furthermore, we discuss the trade-offs between predictive accuracy and model complexity. The latter is assessed by quantifying both the number of material parameters in the discovered models and the computational time required to evaluate the constitutive model and its derivatives. The results show that all methods can reproduce the benchmark data remarkably well. Rather than identifying a single winner, we highlight the strengths and limitations of each approach and provide practical guidance for their use. The source code for all six methods, including the training and comparison scripts, as well as all results and data used in this study, is publicly available via this https URL .

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-17 in cs.AI).
Categorie arXiv: Artificial Intelligence (cs.AI) ; Computational Engineering, Finance, and Science (cs.CE).
Pagina del lavoro: https://arxiv.org/abs/2608.14063 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-19T02:02:02.773Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-08-17?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).