| Grant number: | 23/16712-1 |
| Support Opportunities: | Scholarships in Brazil - Doctorate |
| Start date: | March 01, 2024 |
| End date: | April 30, 2027 |
| Field of knowledge: | Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques |
| Principal Investigator: | Elisa Yumi Nakagawa |
| Grantee: | Wallace Alves Esteves Manzano |
| Host Institution: | Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil |
| Associated research grant: | 23/00488-5 - SPIRA-BM: biomarkers for respiratory conditions on mobile devices using audio analysis with artificial intelligence, AP.TEM |
Abstract Context: The complexity of software systems has increased considerably, particularly in domains like Industry 4.0, Health 4.0, and transportation, which have accompanied the rapid advancement and integration of diverse technologies. These domains have explored digital twins, including autonomous ones, as a promising and differentiating component of their software systems. Problem: Despite the considerable benefits of autonomous digital twins, there is a significant difficulty in designing and maintaining autonomous digital twins that can effectively collect, correlate, and process data, and make decisions. The current literature also lacks comprehensive solutions to address this difficulty. Objective: The general goal of this PhD project is to leverage the autonomous digital twin adoption; more specifically, the goal is to propose a framework that includes a simulation-based method for the development and maintenance of autonomous digital twins, as well as an infrastructure for supporting the method automation. Method: This project encompasses different research methods, including systematic literature review, survey, interviews, case studies, and experiment, each in specific phases/parts of the project. Results: The project intends to contribute to the state of the art of digital twin engineering. It also intends to contribute to software engineering by proposing a new means to deal with a new class of data-driven software systems, which autonomous digital twins are essentially. With those results, we also expect to contribute to the larger research project with which this PhD is associated. | |
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