  Many types of physics-informed neural network models have been proposed in
recent years as approaches for learning solutions to differential equations.
When a particular task requires solving a differential equation at multiple
parameterizations, this requires either re-training the model, or expanding its
representation capacity to include the parameterization -- both solution that
increase its computational cost. We propose the HyperPINN, which uses
hypernetworks to learn to generate neural networks that can solve a
differential equation from a given parameterization. We demonstrate with
experiments on both a PDE and an ODE that this type of model can lead to neural
network solutions to differential equations that maintain a small size, even
when learning a family of solutions over a parameter space.
