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Found 6 papers, 1 papers with code
Epidemic analysis of COVID-19 in China by dynamical modeling
The outbreak of novel coronavirus-caused pneumonia (COVID-19) in Wuhan has attracted worldwide attention.
Populations and Evolution
Revealing hidden dynamics from time-series data by ODENet
To derive the hidden dynamics from observed data is one of the fundamental but also challenging problems in many different fields.
When Machine Learning Meets Multiscale Modeling in Chemical Reactions
Due to the intrinsic complexity and nonlinearity of chemical reactions, direct applications of traditional machine learning algorithms may face with many difficulties.
Identification of hydrodynamic instability by convolutional neural networks
The onset of hydrodynamic instabilities is of great importance in both industry and daily life, due to the dramatic mechanical and thermodynamic changes for different types of flow motions.
Rational evaluation of various epidemic models based on the COVID-19 data of China
In this paper, based on the Akaike information criterion, root mean square error and robustness coefficient, a rational evaluation of various epidemic models/methods, including seven empirical functions, four statistical inference methods and five dynamical models, on their forecasting abilities is carried out.
A data-driven analysis on the mediation effect of compartment models between control measures and COVID-19 epidemics
We make a retrospective review on various control measures taken by 127 countries/territories during the first wave of COVID-19 pandemic until July 7, 2020, and evaluate their impacts on the epidemic dynamics quantitatively.
