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System inference for the spatio-temporal evolution of infectious diseases: Michigan in the time of COVID-19
We extend the classical SIR model of infectious disease spread to account for time dependence in the parameters, which also include diffusivities.
Scale bridging materials physics: Active learning workflows and integrable deep neural networks for free energy function representations in alloys
Specifically, we have developed an integrable deep neural network (IDNN) that can be trained to free energy derivative data obtained from atomic scale models and statistical mechanics, then analytically integrated to recover a free energy density function.
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