Identifiability and predictability of integer- and fractional-order epidemiological models using physics-informed neural networks
Description
We analyze a plurality of epidemiological models through the lens of physics-informed neural networks (PINNs) that enable us to identify time-dependent parameters and data-driven fractional differential operators. In particular, we consider several