Filling the Void: Data-Driven Machine Learning-based Reconstruction of Sampled Spatiotemporal Scientific Simulation Data

As high-performance computing systems continue to advance, the gap between computing performance and I/O capabilities is widening. This bottleneck limits the storage capabilities of increasingly large-scale simulations, which generate data at never-before-seen granularities while only being able to...

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Bibliographic Details
Published in:	SC24-W: Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis pp. 290 - 299
Main Authors:	Biswas, Ayan, Mishra, Aditi, Majumder, Meghanto, Hazarika, Subhashis, Most, Alexander, Castorena, Juan, Bryan, Christopher, McCormick, Patrick, Ahrens, James, Lawrence, Earl, Hagberg, Aric
Format:	Conference Proceeding
Language:	English
Published:	IEEE 17.11.2024
Subjects:	Computational modeling computing methodologies Data models data reduction Deep learning Exascale computing Filling Interpolation Knowledge transfer machine learning machine learning approaches modeling and simulation Neural networks reconstruction scientific visualization simulation types and techniques Spatiotemporal phenomena Training
Online Access:	Get full text
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