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Computational sumulations and artificial inteligence for solving non-newtonian fluid flows

Grant number: 21/07034-4
Support Opportunities:Scholarships in Brazil - Doctorate
Effective date (Start): August 01, 2021
Field of knowledge:Physical Sciences and Mathematics - Mathematics - Applied Mathematics
Principal Investigator:Cassio Machiaveli Oishi
Grantee:Fabio Vinicius Goes Amaral
Host Institution: Faculdade de Ciências e Tecnologia (FCT). Universidade Estadual Paulista (UNESP). Campus de Presidente Prudente. Presidente Prudente , SP, Brazil
Associated research grant:13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry, AP.CEPID
Associated scholarship(s):23/06035-2 - Sparse identification and machine learning techniques on viscoelastic fluid flows, BE.EP.DR


Computational simulations of complex problems governed by differential equations have been significantly improved with advances in Artificial Intelligence (AI) techniques, which can be combined with classical numerical methods. In the current proposal, different AI techniques will be investigated, such as Machine/Deep Learning (ML/DL), Physics Informed Neural Network (PINN), Convolutional Neural Network (CNN) for numerical solutions of mathematical models adopted in incompressible non-newtonian fluid flows. It is worth noting that both the scientific community and industrial sectors have a strong interest in understanding the behavior of non-newtonian fluids that appear in different applications. The study proposal of this project aims to add improvements in finite difference methods for solving viscoelastic fluid flows in confined geometries and domains with moving boundary. AI techniques will be studied not only to accelerate the numerical convergence of classical methods, but also to estimate some dimensionless parameters used in the governing equations. (AU)

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