DeepABM: Scalable and Efficient Agent-Based Simulations Via Geometric Learning Frameworks - a Case Study For Covid-19 Spread and Interventions

Ayush Chopra, Ramesh Raskar, Jayakumar Subramanian, Balaji Krishnamurthy, Esma Senturk Gel, Santiago Romero-Brufau, Kalyan S. Pasupathy, Thomas C. Kingsley. DeepABM: Scalable and Efficient Agent-Based Simulations Via Geometric Learning Frameworks - a Case Study For Covid-19 Spread and Interventions. In Winter Simulation Conference, WSC 2021, Phoenix, AZ, USA, December 12-15, 2021. pages 1-12, IEEE, 2021. [doi]

@inproceedings{ChopraRSKGRPK21,
  title = {DeepABM: Scalable and Efficient Agent-Based Simulations Via Geometric Learning Frameworks - a Case Study For Covid-19 Spread and Interventions},
  author = {Ayush Chopra and Ramesh Raskar and Jayakumar Subramanian and Balaji Krishnamurthy and Esma Senturk Gel and Santiago Romero-Brufau and Kalyan S. Pasupathy and Thomas C. Kingsley},
  year = {2021},
  doi = {10.1109/WSC52266.2021.9715507},
  url = {https://doi.org/10.1109/WSC52266.2021.9715507},
  researchr = {https://researchr.org/publication/ChopraRSKGRPK21},
  cites = {0},
  citedby = {0},
  pages = {1-12},
  booktitle = { Winter Simulation Conference, WSC 2021, Phoenix, AZ, USA, December 12-15, 2021},
  publisher = {IEEE},
  isbn = {978-1-6654-3311-2},
}