  We propose VDL-Surrogate, a view-dependent neural-network-latent-based
surrogate model for parameter space exploration of ensemble simulations that
allows high-resolution visualizations and user-specified visual mappings.
Surrogate-enabled parameter space exploration allows domain scientists to
preview simulation results without having to run a large number of
computationally costly simulations. Limited by computational resources,
however, existing surrogate models may not produce previews with sufficient
resolution for visualization and analysis. To improve the efficient use of
computational resources and support high-resolution exploration, we perform ray
casting from different viewpoints to collect samples and produce compact latent
representations. This latent encoding process reduces the cost of surrogate
model training while maintaining the output quality. In the model training
stage, we select viewpoints to cover the whole viewing sphere and train
corresponding VDL-Surrogate models for the selected viewpoints. In the model
inference stage, we predict the latent representations at previously selected
viewpoints and decode the latent representations to data space. For any given
viewpoint, we make interpolations over decoded data at selected viewpoints and
generate visualizations with user-specified visual mappings. We show the
effectiveness and efficiency of VDL-Surrogate in cosmological and ocean
simulations with quantitative and qualitative evaluations. Source code is
publicly available at https://github.com/trainsn/VDL-Surrogate.
