InstitutDas Team
Jorge Humberto Urrea Quintero

Dr. Jorge Humberto Urrea Quintero

Dr. Jorge Humberto Urrea Quintero
Sprechzeiten
nach Vereinbarung
Adresse
Appelstraße 9a
30167 Hannover
Gebäude
Raum
121
Dr. Jorge Humberto Urrea Quintero
Sprechzeiten
nach Vereinbarung
Adresse
Appelstraße 9a
30167 Hannover
Gebäude
Raum
121

RESEARCH PROJECT

  • Hybrid physics-based and data-driven dynamical systems identification using kernel-based methods
    This project focuses on exploring the different alternatives to assemble so-called hybrid physics-based and data-driven dynamical models and exploring their performance capabilities in engineering tasks such as reliability analysis or closed-loop control. The idea is to combine an optimal linear representation of the system under study-- e.g., optimal in the least square sense-- and extend it to adopt so-called kernel models that can "learn" the system's unmodeled (nonlinear) dynamics. The resulting model is a nonlinear one composed of linear and nonlinear parts. The linear part can be constructed based on some known physics of the real system, which makes it interpretable. The nonlinear part can be identified based on, e.g., measured data computing the error between the linear approximation and the real system. Some well-known kernel models, widely used in Machine Learning applications, could be adopted for its construction, e.g., exponential, square exponential, Matern with parameter 3/2 or 5/2 kernels.
    Leitung: Udo Nackenhorst
    Team: Jorge Urrea
    Jahr: 2022
    Scheme: Hybrid physics-based and data-driven synamical systems identification Scheme: Hybrid physics-based and data-driven synamical systems identification
  • Research

    Research in the area of modeling and control of nonlinear dynamical systems has been one of the main drivers for me since the beginning of my tertiary education process. Following this pathway, I have become a researcher with a strong background in applied mathematics and computational methods in engineering.

    My current research interests are placed on contributing to answering the long-standing question of: how physics-based models can benefit from machine learning techniques to minimize models and real systems mismatches by accurately quantifying their uncertainty sources?

  • Recent publications
    2021 Urrea-Quintero, J. H., Fuhg, J. N., Marino, M., & Fau, A. (2021). PI/PID controller stabilizing sets of uncertain nonlinear systems: an efficient surrogate model-based approach. Nonlinear Dynamics, 105(1), 277-299.
    2020Urrea-Quintero, J. H., Marino, M., Hernandez, H., & Ochoa, S. (2020). Multiscale modeling of a free-radical emulsion polymerization process: Numerical approximation by the Finite Element Method. Computers & Chemical Engineering, 140, 106974.
    2020Urrea-Quintero, J. H., Hernandez, H., & Ochoa, S. (2020). Towards a controllability analysis of multiscale systems: Application of the set-theoretic approach to a semi-batch emulsion polymerization process. Computers & Chemical Engineering, 138, 106833.
  • Teaching Activities
    • Mechanics of Solids (Winter term 2021/22)
    • Stochastic Finite Elements (Summer term 2021 & 2022)