Biography:
Luis Bustinduy de la Guerra, born in Oviedo (Asturias), holds a BSc in Physics from Universidad Complutense de Madrid and an MSc in Industrial Mathematics from Universidad Carlos III de Madrid (UC3M), within an inter-university program run in collaboration with Universidad Politécnica de Madrid (UPM), Universidade da Coruña (UDC), Universidade de Vigo (UVigo), and Universidade de Santiago de Compostela (USC).
He is currently a predoctoral researcher at the Instituto de Investigación Tecnológica (IIT) and a PhD student in the PhD Program in Engineering Systems Modeling at Escuela Técnica Superior de Ingeniería (ICAI), Universidad Pontificia Comillas.
During his undergraduate studies, he completed a one-year Erasmus exchange at Technische Universität München (TUM), where he further focused his interests on statistical physics and computational physics. His work is centered on mathematical modeling of complex systems and scientific machine learning methods. In particular, he develops and applies Physics-Informed Neural Networks (PINNs) to multiscale and homogenization problems in engineering systems.
His Master’s Thesis, “Physics-Informed Neural Networks (PINNs) applied to Multiscale Problems,” represents the first step of his current doctoral research line, aimed at developing new computational strategies for homogenization problems in heterogeneous media. His background combines computational physics research, numerical simulation, and model validation in both academic and applied environments.
Areas of interest:
Mathematical modeling of engineering systems, Scientific Machine Learning, Physics-Informed Neural Networks (PINNs), multiscale modeling and homogenization, statistical physics, phase transitions and critical phenomena, numerical methods for PDEs including FEM and BEM, and computational simulation of complex systems.
Experience:
At Universidad Carlos III de Madrid, at the Gregorio Millán Barbany Institute, he collaborated as a research assistant from September 2024 to March 2026. During this period, he worked on spatiotemporal modeling of epithelial tissues, supporting validation and improvement tasks for computational tools based on the Active Vertex Model (AVM). This stage allowed him to keep learning in the analysis of phase transitions, bifurcation phenomena, and cell mobility, while reinforcing his interest in numerical simulation of complex systems.
Previously, he completed an internship at Tecnatom S.A. (ITER Project) from November 2022 to March 2023, as part of the Assembly VV team. In that role, he contributed to the development and adaptation of ultrasound inspection techniques for nuclear fusion components, especially the Vacuum Vessel, as well as to the definition and validation of technical procedures under industrial requirements.
