An introduction to sparse identification using artificial intelligence techniques

• Grant number: 26/11713-8
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: July 01, 2026
• End date: June 30, 2027
• Field of knowledge: Physical Sciences and Mathematics - Mathematics - Applied Mathematics
• Principal Investigator: Cassio Machiaveli Oishi
• Grantee: Gabriel Ciriaco de Carvalho
• Host Institution: Faculdade de Ciências e Tecnologia (FCT). Universidade Estadual Paulista (UNESP). Campus de Presidente Prudente. Presidente Prudente , SP, Brazil
• Company: Universidade de São Paulo (USP). Instituto de Ciências Matemáticas e de Computação (ICMC)
• Associated research grant: 23/14427-8 - Data Science for Smart Industry (CDII), AP.PCPE

Abstract

The study proposed in this project provides an introductory analysis of the concept of sparse identification techniques in non-linear dynamical systems. Using concepts from numerical linear algebra, such as linear regression, norms, and matrix analysis, the student will study the construction of a methodology that applies a machine learning algorithm to solve the convex optimization problem in the l1 norm in order to identify equations governing a given dynamical system. The project will evaluate the theoretical details of this methodology and also the computational algorithms, with applications in systems of ordinary differential equations, such as the SIRD model used in the study of epidemics. (AU)