  We present a partitioned neural network-based framework for learning of
fluid-structure interaction (FSI) problems. We decompose the simulation domain
into two smaller sub-domains, i.e., fluid and solid domains, and incorporate an
independent neural network for each. A library is used to couple the two
networks which takes care of boundary data communication, data mapping and
equation coupling. Simulation data are used for training of the both neural
networks. We use a combination of convolutional and recurrent neural networks
(CNN and RNN) to account for both spatial and temporal connectivity. A
quasi-Newton method is used to accelerate the FSI coupling convergence. We
observe a very good agreement between the results of the presented framework
and the classical numerical methods for simulation of 1d fluid flow inside an
elastic tube. This work is a preliminary step for using neural networks to
speed-up the FSI coupling convergence by providing an accurate initial guess in
each time step for classical numerical solvers
