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Predicting Supercapacitor Performance Using Reinforcement Learning Methodology
In this proposal, intern will be tasked with reinforcement learning algorithm to refine the process of material selection. This method of machine learning is designed to iteratively enhance decision-making, ensuring the most effective materials are identified for various applications. The focus will be on aligning the materials’ performance characteristics with their intended use. As a result, the partner company will gain access to innovative approaches in material selection that could lead to improved product development and a stronger market position.
Faculty Supervisor:
Hadis Zarrin
Student:
Partner:
Sensofine Inc
Discipline:
Engineering
Sector:
Manufacturing
University:
Toronto Metropolitan University
Program:
Accelerate
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Mitacs is funded by the Government of Canada, the Government of Alberta, the Government of British Columbia, Research Manitoba, the Government of New Brunswick, the Government of Newfoundland and Labrador, the Government of Nova Scotia, the Government of Ontario, Innovation PEI, the Government of Quebec, the Government of Saskatchewan, and the Government of Yukon.