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Found 7 papers, 2 papers with code
Machine Learning Techniques for Data Reduction of CFD Applications
We present an approach called guaranteed block autoencoder that leverages Tensor Correlations (GBATC) for reducing the spatiotemporal data generated by computational fluid dynamics (CFD) and other scientific applications.
MGARD: A multigrid framework for high-performance, error-controlled data compression and refactoring
We describe MGARD, a software providing MultiGrid Adaptive Reduction for floating-point scientific data on structured and unstructured grids.
Scalable Hybrid Learning Techniques for Scientific Data Compression
Data compression is becoming critical for storing scientific data because many scientific applications need to store large amounts of data and post process this data for scientific discovery.
Expressing linear equality constraints in feedforward neural networks
Elimination of the auxiliary variables leads to a dual minimization problem on the Lagrange multipliers introduced to satisfy the linear constraints.
Hybrid Generative Models for Two-Dimensional Datasets
Two-dimensional array-based datasets are pervasive in a variety of domains.
Neural data compression for physics plasma simulation
We present a VAE-based data compression method, called VAe Physics Optimized Reduction (VAPOR), to compress scientific data while preserving physics constraints.
A Hidden Variables Approach to Multilabel Logistic Regression
Multilabel classification is an important problem in a wide range of domains such as text categorization and music annotation.
