Constrained Autoencoders: Incorporating equality constraints in learned scientific data compression

In scientific data compression, it is crucial to preserve Quantities of Interest (QoI) derived from the data for accurate post-analysis of scientific applications. In this work, we present Constrained Autoencoders (CAEs) where we impose linear QoI as constraints on neural network activations. We circumvent the difficulty of using standard convex optimization methods on the output predictor in the context of autoencoder-driven compression.