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Constrained Autoencoders: Incorporating equality constraints in learned scientific data compression

As today's scientific simulations with high-performance computing produce an extremely large amount of data, reliable data compression techniques are becoming important. In scientific applications, downstream quantities derived from the original or primary data (PD) are crucial for post-analysis, and therefore these Quantities of Interest (QoI) are required to be preserved during compression. While autoencoders (AEs) have recently been used for the compression of scientific datasets, there is a clear lack in the literature w.r.t.

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