Deep Learning-Based Feature-Aware Data Modeling for Complex Physics Simulations

8 Dec 2019 · Qun Liu, Subhashis Hazarika, John M. Patchett, James Paul Ahrens, Ayan Biswas ·

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.

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AutoEncoder • Batch Normalization • Concatenated Skip Connection • Convolution • Dense Block • ReLU • Test

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Deep Learning-Based Feature-Aware Data Modeling for Complex Physics Simulations

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