DyCMaMod. Dinámica y control para el aprendizaje automático y la Modelización
Project reference. PID2023-146872OB-I00
Funded by: MINECO. Ministerio de Economía, Comercio y Empresa
Principal Investigator (PI): Prof. Enrique Zuazua, Prof. Rafael Orive
Host Institution: DeustoCCM, University of Deusto, Spain
Duration: 01.09.2024 – 31.08.2027
Goal
DyCMaMod seeks to develop mathematical methodologies to improve the interpretation, reliability, and privacy of Artificial Intelligence and Machine Learning models. Our main motivation focuses on the creation of Digital Twins, integrating mechanical models with empirical data to create hybrid systems, applicable in sectors such as transportation and energy.
Objectives
DyCMaMod focuses on integrating mechanical models with empirical data to create hybrid models.
This will be achieved through:
1. Generation of synthetic models using Neural Networks and reservoir computing to integrate real-time data.
2. Development of an innovative “collapse strategy” to minimize the discrepancy between synthetic and mechanical models.
3. Application of control techniques to adapt models to empirical data.
4. Use of methods based on particle dynamics, collective behavior, and kinetic equations.
5. Fusion of the “Random Batch Method” with Model PRedictive Control to reduce computational costs.
6. Creation of a computational platform with public access through DyCMaMod-GitHub.
