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Sparse identification and machine learning techniques on viscoelastic fluid flows

Grant number: 23/06035-2
Support Opportunities:Scholarships abroad - Research Internship - Doctorate
Effective date (Start): September 01, 2023
Effective date (End): August 31, 2024
Field of knowledge:Physical Sciences and Mathematics - Mathematics - Applied Mathematics
Principal Investigator:Cassio Machiaveli Oishi
Grantee:Fabio Vinicius Goes Amaral
Supervisor: Steven Brunton
Host Institution: Faculdade de Ciências e Tecnologia (FCT). Universidade Estadual Paulista (UNESP). Campus de Presidente Prudente. Presidente Prudente , SP, Brazil
Research place: University of Washington, United States  
Associated to the scholarship:21/07034-4 - Computational sumulations and artificial inteligence for solving non-newtonian fluid flows, BP.DR

Abstract

In this project we will investigate the combination of Sparse Identification Non-linear Dynamics (SINDy) and AutoEncoders (AE) for studying viscoelastic fluid flows. SINDy is a valuable tool for identifying the governing equations of a system based on observational data, while AE can generate reduced-order models that make solving fluid flow problems more computationally efficient. This combination of techniques can lead to a better understanding of the complex behavior exhibited by viscoelastic fluids, and can help develop more accurate models for simulating and analyzing parametrized systems. (AU)

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