Reconstruction of Complex Systems from Data

• Grant number: 18/22503-8
• Support Opportunities: Research Grants - Visiting Researcher Grant - Brazil
• Start date: February 01, 2019
• End date: January 31, 2020
• Field of knowledge: Physical Sciences and Mathematics - Mathematics - Applied Mathematics
• Principal Investigator: Tiago Pereira da Silva
• Grantee: Tiago Pereira da Silva
• Visiting researcher: Marcel Novaes
• Visiting researcher institution: Universidade Federal de Uberlândia (UFU). Instituto de Física, Brazil
• Host Institution: Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil
• Associated research grant: 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry, AP.CEPID

Abstract

This proposal addresses the problem of predicting the occurrence of sudden changes of behaviour in complex systems from data. Such abrupt changes in behaviour are known as critical transitions and are observed in ecology, neuroscience, medicine, and technology. The traditional mathematical theory fails to address these novel challenges, as these complex systems have a wild global behaviour. The recent results by the supervisor and collaborators explains how the interactions affect the system and opens the possibility to predict critical transitions by reconstructing the network dynamics from data. This gives a unique opportunity to address the major open problemReconstruction of network dynamics from data. Because we understand the emergent properties of the network, from a single time-series we can reverse engineer the problem and obtain a model from data. Thereby, we can predict critical transitions from data which would be impossible otherwise.This leads to a major breakthrough and provides models to understand critical transitions and thereby develop strategies to help to prevent catastrophes. Through a network of collaborators, we will apply the reconstruction approach to neuroscience and engineering. (AU)