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Found 3 papers, 3 papers with code
Neural parameter calibration and uncertainty quantification for epidemic forecasting
The recent COVID-19 pandemic has thrown the importance of accurately forecasting contagion dynamics and learning infection parameters into sharp focus.
Inferring networks from time series: a neural approach
Network structures underlie the dynamics of many complex phenomena, from gene regulation and foodwebs to power grids and social media.
Neural parameter calibration for large-scale multi-agent models
Computational models have become a powerful tool in the quantitative sciences to understand the behaviour of complex systems that evolve in time.
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