DeepABM: Scalable, efficient and differentiable agent-based simulations\n via graph neural networks Article Swipe
Ayush Chopra
,
Esma S. Gel
,
Jayakumar Subramanian
,
Balaji Krishnamurthy
,
Santiago Romero‐Brufau
,
Kalyan S. Pasupathy
,
Thomas C. Kingsley
,
Ramesh Raskar
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2110.04421
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2110.04421
We introduce DeepABM, a framework for agent-based modeling that leverages\ngeometric message passing of graph neural networks for simulating action and\ninteractions over large agent populations. Using DeepABM allows scaling\nsimulations to large agent populations in real-time and running them\nefficiently on GPU architectures. To demonstrate the effectiveness of DeepABM,\nwe build DeepABM-COVID simulator to provide support for various\nnon-pharmaceutical interventions (quarantine, exposure notification,\nvaccination, testing) for the COVID-19 pandemic, and can scale to populations\nof representative size in real-time on a GPU. Specifically, DeepABM-COVID can\nmodel 200 million interactions (over 100,000 agents across 180 time-steps) in\n90 seconds, and is made available online to help researchers with modeling and\nanalysis of various interventions. We explain various components of the\nframework and discuss results from one research study to evaluate the impact of\ndelaying the second dose of the COVID-19 vaccine in collaboration with clinical\nand public health experts. While we simulate COVID-19 spread, the ideas\nintroduced in the paper are generic and can be easily extend to other forms of\nagent-based simulations. Furthermore, while beyond scope of this document,\nDeepABM enables inverse agent-based simulations which can be used to learn\nphysical parameters in the (micro) simulations using gradient-based\noptimization with large-scale real-world (macro) data. We are optimistic that\nthe current work can have interesting implications for bringing ABM and AI\ncommunities closer.\n
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2110.04421
- https://arxiv.org/pdf/2110.04421
- OA Status
- green
- Cited By
- 3
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4298202990
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4298202990Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2110.04421Digital Object Identifier
- Title
-
DeepABM: Scalable, efficient and differentiable agent-based simulations\n via graph neural networksWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-10-08Full publication date if available
- Authors
-
Ayush Chopra, Esma S. Gel, Jayakumar Subramanian, Balaji Krishnamurthy, Santiago Romero‐Brufau, Kalyan S. Pasupathy, Thomas C. Kingsley, Ramesh RaskarList of authors in order
- Landing page
-
https://arxiv.org/abs/2110.04421Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2110.04421Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2110.04421Direct OA link when available
- Concepts
-
Computer science, Scalability, Distributed computing, Differentiable function, Artificial intelligence, Machine learning, Theoretical computer science, Mathematical analysis, Database, MathematicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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3Total citation count in OpenAlex
- Citations by year (recent)
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2024: 1, 2023: 1, 2022: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.learn\nphysical | 173 |
| abstract_inverted_index.of\nagent-based | 155 |
| abstract_inverted_index.populations\nof | 67 |
| abstract_inverted_index.and\ninteractions | 19 |
| abstract_inverted_index.ideas\nintroduced | 141 |
| abstract_inverted_index.them\nefficiently | 36 |
| abstract_inverted_index.document,\nDeepABM | 163 |
| abstract_inverted_index.leverages\ngeometric | 9 |
| abstract_inverted_index.scaling\nsimulations | 27 |
| abstract_inverted_index.notification,\nvaccination, | 57 |
| abstract_inverted_index.various\nnon-pharmaceutical | 53 |
| abstract_inverted_index.gradient-based\noptimization | 180 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 8 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.6700000166893005 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.55663881 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |