  We propose GNN-Surrogate, a graph neural network-based surrogate model to
explore the parameter space of ocean climate simulations. Parameter space
exploration is important for domain scientists to understand the influence of
input parameters (e.g., wind stress) on the simulation output (e.g.,
temperature). The exploration requires scientists to exhaust the complicated
parameter space by running a batch of computationally expensive simulations.
Our approach improves the efficiency of parameter space exploration with a
surrogate model that predicts the simulation outputs accurately and
efficiently. Specifically, GNN-Surrogate predicts the output field with given
simulation parameters so scientists can explore the simulation parameter space
with visualizations from user-specified visual mappings. Moreover, our
graph-based techniques are designed for unstructured meshes, making the
exploration of simulation outputs on irregular grids efficient. For efficient
training, we generate hierarchical graphs and use adaptive resolutions. We give
quantitative and qualitative evaluations on the MPAS-Ocean simulation to
demonstrate the effectiveness and efficiency of GNN-Surrogate. Source code is
publicly available at https://github.com/trainsn/GNN-Surrogate.
