DescriptionData 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 employ integrated skip connections to obtain higher performance compared to the existing autoencoders. Our experiments demonstrate the initial success of the proposed framework and create optimism for the in situ use case.