Undergraduate Research on Physics-Informed Graph Attention Networks for COVID-19 Prediction
The COVID-19 pandemic has significantly impacted most countries in the world. Analyzing COVID-19 data from these countries together is a prominent challenge. Under the sponsorship of NSF REU, this paper describes our experience with a ten-week project that aims to guide an REU scholar to develop a p...
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International Institute of Informatics and Cybernetics
2022-10-01
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| Series: | Journal of Systemics, Cybernetics and Informatics |
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| Online Access: | http://www.iiisci.org/Journal/PDV/sci/pdfs/ZA280UJ22.pdf
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| author | Yu Liang Dalei Wu |
| author_facet | Yu Liang Dalei Wu |
| author_sort | Yu Liang |
| collection | DOAJ |
| description | The COVID-19 pandemic has significantly impacted most countries in the world. Analyzing COVID-19 data from these countries together is a prominent challenge. Under the sponsorship of NSF REU, this paper describes our experience with a ten-week project that aims to guide an REU scholar to develop a physics-guided graph attention network to predict the global COVID- 19 Pandemics. We mainly presented the preparation, implementation, and dissemination of the addressed project. The COVID-19 situation in a country could be dramatically different from that of others, which suggests that COVID-19 pandemic data are generated based on different mechanisms, making COVID-19 data in different countries follow different probability distributions. Learning more than one hundred underlying probability distributions for countries in the world from large scale COVID- 19 data is beyond a single machine learning model. To address this challenge, we proposed two team-learning frameworks for predicting the COVID-19 pandemic trends: peer learning and layered ensemble learning framework. This addressed framework assigns an adaptive physics-guided graph attention network (GAT) to each learning agent. All the learning agents are fabricated in a hierarchical architecture, which enables agents to collaborate with each other in peer-to-peer and cross-layer way. This layered architecture shares the burden of large-scale data processing on machine learning models of all units. Experiments are run to verify the effectiveness of our approaches. The results indicate the proposed ensemble outperforms baseline methods. Besides being documented on GitHub, this work has resulted in two journal papers. |
| format | Article |
| id | doaj-art-3d28c544c96a4a47a44abbdb435be708 |
| institution | OA Journals |
| issn | 1690-4524 |
| language | English |
| publishDate | 2022-10-01 |
| publisher | International Institute of Informatics and Cybernetics |
| record_format | Article |
| series | Journal of Systemics, Cybernetics and Informatics |
| spelling | doaj-art-3d28c544c96a4a47a44abbdb435be7082025-08-20T02:20:16ZengInternational Institute of Informatics and CyberneticsJournal of Systemics, Cybernetics and Informatics1690-45242022-10-01205148159Undergraduate Research on Physics-Informed Graph Attention Networks for COVID-19 PredictionYu LiangDalei WuThe COVID-19 pandemic has significantly impacted most countries in the world. Analyzing COVID-19 data from these countries together is a prominent challenge. Under the sponsorship of NSF REU, this paper describes our experience with a ten-week project that aims to guide an REU scholar to develop a physics-guided graph attention network to predict the global COVID- 19 Pandemics. We mainly presented the preparation, implementation, and dissemination of the addressed project. The COVID-19 situation in a country could be dramatically different from that of others, which suggests that COVID-19 pandemic data are generated based on different mechanisms, making COVID-19 data in different countries follow different probability distributions. Learning more than one hundred underlying probability distributions for countries in the world from large scale COVID- 19 data is beyond a single machine learning model. To address this challenge, we proposed two team-learning frameworks for predicting the COVID-19 pandemic trends: peer learning and layered ensemble learning framework. This addressed framework assigns an adaptive physics-guided graph attention network (GAT) to each learning agent. All the learning agents are fabricated in a hierarchical architecture, which enables agents to collaborate with each other in peer-to-peer and cross-layer way. This layered architecture shares the burden of large-scale data processing on machine learning models of all units. Experiments are run to verify the effectiveness of our approaches. The results indicate the proposed ensemble outperforms baseline methods. Besides being documented on GitHub, this work has resulted in two journal papers.http://www.iiisci.org/Journal/PDV/sci/pdfs/ZA280UJ22.pdf physics-guided learninggraph attention networklayered ensemble learningcovid-19 |
| spellingShingle | Yu Liang Dalei Wu Undergraduate Research on Physics-Informed Graph Attention Networks for COVID-19 Prediction Journal of Systemics, Cybernetics and Informatics physics-guided learning graph attention network layered ensemble learning covid-19 |
| title | Undergraduate Research on Physics-Informed Graph Attention Networks for COVID-19 Prediction |
| title_full | Undergraduate Research on Physics-Informed Graph Attention Networks for COVID-19 Prediction |
| title_fullStr | Undergraduate Research on Physics-Informed Graph Attention Networks for COVID-19 Prediction |
| title_full_unstemmed | Undergraduate Research on Physics-Informed Graph Attention Networks for COVID-19 Prediction |
| title_short | Undergraduate Research on Physics-Informed Graph Attention Networks for COVID-19 Prediction |
| title_sort | undergraduate research on physics informed graph attention networks for covid 19 prediction |
| topic | physics-guided learning graph attention network layered ensemble learning covid-19 |
| url | http://www.iiisci.org/Journal/PDV/sci/pdfs/ZA280UJ22.pdf
|
| work_keys_str_mv | AT yuliang undergraduateresearchonphysicsinformedgraphattentionnetworksforcovid19prediction AT daleiwu undergraduateresearchonphysicsinformedgraphattentionnetworksforcovid19prediction |