• Corpus ID: 238583327

DeepABM: Scalable, efficient and differentiable agent-based simulations via graph neural networks

@article{Chopra2021DeepABMSE,
  title={DeepABM: Scalable, efficient and differentiable agent-based simulations via graph neural networks},
  author={Ayush Chopra and Esma Senturk Gel and Jayakumar Subramanian and Balaji Krishnamurthy and Santiago Romero-Brufau and Kalyan Sunder Pasupathy and Thomas C. Kingsley and Ramesh Raskar},
  journal={ArXiv},
  year={2021},
  volume={abs/2110.04421}
}
We introduce DeepABM, a framework for agent-based modeling that leverages geometric message passing of graph neural networks for simulating action and interactions over large agent populations. Using DeepABM allows scaling simulations to large agent populations in real-time and running them efficiently on GPU architectures. To demonstrate the effectiveness of DeepABM, we build DeepABM-COVID simulator to provide support for various non-pharmaceutical interventions (quarantine, exposure… 
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