We qualitatively and quantitatively evaluate the effectiveness and efficiency of latent representations generated by our method with data from multiple scientific visualization applications.
In the model inference stage, we predict the latent representations at previously selected viewpoints and decode the latent representations to data space.
Our approach improves the efficiency of parameter space exploration with a surrogate model that predicts the simulation outputs accurately and efficiently.
Second, we propose a workload estimation model, helping RL agents estimate the workload distribution of processes in future computations.
We present an approach for hierarchical super resolution (SR) using neural networks on an octree data representation.