ArticleProceedings of the National Academy of Sciences of the United States of America2022
Digital rheometer twins: Learning the hidden rheology of complex fluids through rheology-informed graph neural networks.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- AI-driven 3D bioprinting for regenerative medicine: From bench to bedside.Bioactive materials · 2025Review
- Unbiased construction of constitutive relations for soft materials from experiments via rheology-informed neural networks.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Scientific machine learning for modeling and simulating complex fluids.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- "Gloppiness" Phenomena and a Computer Vision Method to Quantify It.Gels (Basel, Switzerland) · 2023Article
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4 authors.
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Abstract
SignificanceScience-based data-driven methods that can describe the rheological behavior of complex fluids can be transformative across many disciplines. Digital rheometer twins, which are developed here, can significantly reduce the cost, time, and energy required to characterize complex fluids and predict their future behavior. This is made possible by combining two different methods of informing neural networks with the rheological underpinnings of a system, resulting in quantitative recovery of a gel's response to different flow protocols. The platform developed here is general enough that it can be extended to areas well beyond complex fluids modeling.
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