Rheology-Informed Neural Networks (RhINNs) for forward and inverse metamodelling of complex fluids

Abstract 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 fl...

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Autores principales:	Mohammadamin Mahmoudabadbozchelou, Safa Jamali
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/f40858f843874b4290fb48c970ebab93
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spelling	oai:doaj.org-article:f40858f843874b4290fb48c970ebab932021-12-02T17:47:18ZRheology-Informed Neural Networks (RhINNs) for forward and inverse metamodelling of complex fluids10.1038/s41598-021-91518-32045-2322https://doaj.org/article/f40858f843874b4290fb48c970ebab932021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-91518-3https://doaj.org/toc/2045-2322Abstract 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 under various flow protocols. We present Rheology-Informed Neural Networks (RhINNs) for solving systems of Ordinary Differential Equations (ODEs) adopted for complex fluids. The proposed RhINNs are employed to solve the constitutive models with multiple ODEs by benefiting from Automatic Differentiation in neural networks. In a direct solution, the RhINNs platform accurately predicts the fully resolved solution of constitutive equations for a Thixotropic-Elasto-Visco-Plastic (TEVP) complex fluid for a series of flow protocols. From a practical perspective, an exhaustive list of experiments are required to identify model parameters for a multi-variant constitutive TEVP model. RhINNs are found to learn these non-trivial model parameters for a complex material using a single flow protocol, enabling accurate modeling with limited number of experiments and at an unprecedented rate. We also show the RhINNs are not limited to a specific model and can be extended to include various models and recover complex manifestations of kinematic heterogeneities and transient shear banding of thixotropic fluids.Mohammadamin MahmoudabadbozchelouSafa JamaliNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Mohammadamin Mahmoudabadbozchelou Safa Jamali Rheology-Informed Neural Networks (RhINNs) for forward and inverse metamodelling of complex fluids
description	Abstract 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 under various flow protocols. We present Rheology-Informed Neural Networks (RhINNs) for solving systems of Ordinary Differential Equations (ODEs) adopted for complex fluids. The proposed RhINNs are employed to solve the constitutive models with multiple ODEs by benefiting from Automatic Differentiation in neural networks. In a direct solution, the RhINNs platform accurately predicts the fully resolved solution of constitutive equations for a Thixotropic-Elasto-Visco-Plastic (TEVP) complex fluid for a series of flow protocols. From a practical perspective, an exhaustive list of experiments are required to identify model parameters for a multi-variant constitutive TEVP model. RhINNs are found to learn these non-trivial model parameters for a complex material using a single flow protocol, enabling accurate modeling with limited number of experiments and at an unprecedented rate. We also show the RhINNs are not limited to a specific model and can be extended to include various models and recover complex manifestations of kinematic heterogeneities and transient shear banding of thixotropic fluids.
format	article
author	Mohammadamin Mahmoudabadbozchelou Safa Jamali
author_facet	Mohammadamin Mahmoudabadbozchelou Safa Jamali
author_sort	Mohammadamin Mahmoudabadbozchelou
title	Rheology-Informed Neural Networks (RhINNs) for forward and inverse metamodelling of complex fluids
title_short	Rheology-Informed Neural Networks (RhINNs) for forward and inverse metamodelling of complex fluids
title_full	Rheology-Informed Neural Networks (RhINNs) for forward and inverse metamodelling of complex fluids
title_fullStr	Rheology-Informed Neural Networks (RhINNs) for forward and inverse metamodelling of complex fluids
title_full_unstemmed	Rheology-Informed Neural Networks (RhINNs) for forward and inverse metamodelling of complex fluids
title_sort	rheology-informed neural networks (rhinns) for forward and inverse metamodelling of complex fluids
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/f40858f843874b4290fb48c970ebab93
work_keys_str_mv	AT mohammadaminmahmoudabadbozchelou rheologyinformedneuralnetworksrhinnsforforwardandinversemetamodellingofcomplexfluids AT safajamali rheologyinformedneuralnetworksrhinnsforforwardandinversemetamodellingofcomplexfluids
_version_	1718379503718236160

Rheology-Informed Neural Networks (RhINNs) for forward and inverse metamodelling of complex fluids

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