KiLearn

Project name: KiLearn – Kinetic equations and Learning control
Project reference: PID2020-112617GB-C22
Funding source: MINECO
Duration: September 2021 – September 2024
Principal Investigator (PI): Miguel Escobedo (UPV/EHU), Enrique Zuazua (Deusto Foundation)

About the Project

KiLearn The project arises from the necessity to deepen the understanding of several fundamental questions in Machine Learning, quantum gases, and protein’s dynamics through a detailed study of the mathematical properties of some models that are currently used in those fields. These questions include the time evolution of a Bose-Einstein condensate, the fragmentation dynamics of proteins from experiments, or the impact of the architecture on the performances of a Neural Network.

The increasing interconnection between Machine Learning and kinetic theory motivates a research path that develops on the boundary between these two worlds and has the potential of leading to deep and breakthrough results in both areas. Starting from this hypothesis, the general objective of the research project KiLearn is to develop new Machine Learning-inspired mathematical tools for the analysis of Kinetic Equations and, at the same time, to extend the present knowledge in Machine Learning and Neural Networks taking advantage of the vast theoretical baggage that kinetic theory has to offer.

Project Members

-Miguel Escobedo – UPV/EHU, University of the Basque Country
–Enrique Zuazua – Deusto Foundation
–Umberto Biccari – Deusto Foundation
-Iker Pastor – University of Deusto

Publications