  Experimental data is often comprised of variables measured independently, at
different sampling rates (non-uniform ${\Delta}$t between successive
measurements); and at a specific time point only a subset of all variables may
be sampled. Approaches to identifying dynamical systems from such data
typically use interpolation, imputation or subsampling to reorganize or modify
the training data $\textit{prior}$ to learning. Partial physical knowledge may
also be available $\textit{a priori}$ (accurately or approximately), and
data-driven techniques can complement this knowledge. Here we exploit neural
network architectures based on numerical integration methods and $\textit{a
priori}$ physical knowledge to identify the right-hand side of the underlying
governing differential equations. Iterates of such neural-network models allow
for learning from data sampled at arbitrary time points $\textit{without}$ data
modification. Importantly, we integrate the network with available partial
physical knowledge in "physics informed gray-boxes"; this enables learning
unknown kinetic rates or microbial growth functions while simultaneously
estimating experimental parameters.
