Felipe CORTES JARAMILLO

Ph.D. Student in Applied Machine Learning

Computer scientist with a Master’s degree in Artificial Intelligence and Data Mining, and professional experience as a data engineer and data scientist. My research experience ranges from computer vision to physics-informed machine learning.

Since October 2024, I have been a Ph.D. student in a collaboration between IFP Energies Nouvelles (IFPEN) and Lorraine University, focusing on the integration of artificial intelligence and kinetic models for catalytic reactions. The goal of my research is to develop optimal methodologies for combining prior reaction knowledge with AI models to reduce development time and improve the performance of classical kinetic models.

This Ph.D. follows an internship at IFPEN, during which I developed a physics-informed neural network (PINN) for the hydrocracking reactor process. The resulting hybrid framework demonstrated improvements over traditional kinetic models. These results will be further presented in upcoming conferences and publications in 2025.

CV
Linkedin

felipe.cortes-jaramillo@ifpen.fr

Promotion 2024-2027

Publications

Felipe Cortes Jaramillo, Benoit Celse, Per Julian Becker, Thibault Faney, Victor Costa, Jean-Marc Commenge,
Integrating kinetic ODEs in neural networks: A physics-informed hybrid approach to reactor modeling,
Digital Chemical Engineering,
Volume 20,
2026,
100325,
ISSN 2772-5081,
https://doi.org/10.1016/j.dche.2026.100325.

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