Advancing agent-based simulation scalability
High-fidelity simulation models provide the most accurate representation of the spread of COVID and related diseases but are subject to computational limitations. Agent-based modelling, which treats each individual as a unique agent with objectives/properties/etc., is an example of such a high-fidelity model. The project’s objective is to realize a vastly improved scalability of this type of analysis, using modern techniques in the field of deep learning and AI.
  • Specifically in representation learning of dynamical processes.
  • Co-Project Investigators: Christian Muise (Queen’s University), Gias Uddin (University of Calgary), Manos Papagelis (York University) and Morgan Craig (Université de Montréal)
Networks »Networks
OMNI – Research »OMNI – Research
04 Intervention and Control »04 Intervention and Control
Advancing agent-based simulation scalability
Christian Muise »Christian Muise
Gias Uddin »Gias Uddin
Manos Papagelis »Manos Papagelis
Morgan Craig »Morgan Craig
Covid-19 »Covid-19
Artificial intelligence »Artificial intelligence
Simulation »Simulation
Agent-based models »Agent-based models
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