Atrial constitutive neural networks
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866915226165706752 |
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| author | Peirlinck, Mathias Linka, Kevin Kuhl, Ellen |
| author_facet | Peirlinck, Mathias Linka, Kevin Kuhl, Ellen |
| contents | This work presents a novel approach for characterizing the mechanical behavior of atrial tissue using constitutive neural networks. Based on experimental biaxial tensile test data of healthy human atria, we automatically discover the most appropriate constitutive material model, thereby overcoming the limitations of traditional, pre-defined models. This approach offers a new perspective on modeling atrial mechanics and is a significant step towards improved simulation and prediction of cardiac health. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_02748 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Atrial constitutive neural networks Peirlinck, Mathias Linka, Kevin Kuhl, Ellen Computational Engineering, Finance, and Science Soft Condensed Matter Machine Learning Medical Physics Tissues and Organs This work presents a novel approach for characterizing the mechanical behavior of atrial tissue using constitutive neural networks. Based on experimental biaxial tensile test data of healthy human atria, we automatically discover the most appropriate constitutive material model, thereby overcoming the limitations of traditional, pre-defined models. This approach offers a new perspective on modeling atrial mechanics and is a significant step towards improved simulation and prediction of cardiac health. |
| title | Atrial constitutive neural networks |
| topic | Computational Engineering, Finance, and Science Soft Condensed Matter Machine Learning Medical Physics Tissues and Organs |
| url | https://arxiv.org/abs/2504.02748 |