Sparse identification and machine learning techniques on viscoelastic fluid flows

• Grant number: 23/06035-2
• Support Opportunities: Scholarships abroad - Research Internship - Doctorate
• Start date: September 01, 2023
• End date: 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
• Institution abroad: 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)