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

• DOI: 10.48550/arxiv.1912.03587
• Publication Date: 2019-01-01
• Authors: Liu, Qun; Hazarika, Subhashis; Patchett, John M.; Ahrens, James Paul; Biswas, Ayan
• Keywords: FOS: Computer and information sciences; Computer Science - Machine Learning; Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV); Computer Science - Computer Vision and Pattern Recognition; 0202 electrical engineering, electronic engineering, information engineering; FOS: Electrical engineering, electronic engineering, information engineering; 02 engineering and technology; Electrical Engineering and Systems Science - Image and Video Processing; Machine Learning (cs.LG)

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.<br/>Accepted as a research poster at the International Conference for High Performance Computing, Networking, Storage, and Analysis (SC19)<br/>