  Data modeling and reduction for in situ is important. Feature-driven methods
for in situ data analysis and reduction are a priority for future exascale
machines as there are currently very few such methods. We investigate a
deep-learning based workflow that targets in situ data processing using
autoencoders. We propose a Residual Autoencoder integrated Residual in Residual
Dense Block (RRDB) to obtain better performance. Our proposed framework
compressed our test data into 66 KB from 2.1 MB per 3D volume timestep.
