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Rheology-Informed Neural Networks (RhINNs) for forward and inverse metamodelling of complex fluids
Reliable and accurate prediction of complex fluids’ response under flow is of great interest across many disciplines, from biological systems to virtually all soft materials. The challenge is to solve non-trivial time and rate dependent constitutive equations to describe these structured fluids unde...
| Autores principales: | , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
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Nature Publishing Group UK
2021
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| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8187644/ https://www.ncbi.nlm.nih.gov/pubmed/34103602 http://dx.doi.org/10.1038/s41598-021-91518-3 |