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Theoretical guarantees of convergence and novel machine learning methods for computational algorithms for Variational Mean-Field Games
The project focuses on Variational Mean-Field Games: it will provide theoretical guarantees for the convergence of numerical algorithms currently used and possibly develop also novel machine learning methods for it.
Faculty Supervisor:
Aaron Smith
Student:
Partner:
Università Degli Studi Di Genova
Discipline:
Mathematics
Sector:
Life Sciences (not health); Artificial Intelligence
University:
University of Ottawa
Program:
Globalink Research Award
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