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Non-invasive Inference of Thrombus Material Properties with Physics-Informed Neural Networks
We employ physics-informed neural networks (PINNs) to infer properties of biological materials using synthetic data. In particular, we successfully apply PINNs on inferring permeability and viscoelastic modulus from thrombus deformation data, which can be described by the fourth-order Cahn-Hilliard...
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| Publicado no: | Comput Methods Appl Mech Eng |
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| Main Authors: | , , , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
2020
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7785048/ https://ncbi.nlm.nih.gov/pubmed/33414569 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1016/j.cma.2020.113603 |
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