  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. It uses a
multidimensional block of tensors (spanning in space and time) for both input
and output, capturing the spatiotemporal and interspecies relationship within a
tensor. The tensor consists of species that represent different elements in a
CFD simulation. To guarantee the error bound of the reconstructed data,
principal component analysis (PCA) is applied to the residual between the
original and reconstructed data. This yields a basis matrix, which is then used
to project the residual of each instance. The resulting coefficients are
retained to enable accurate reconstruction. Experimental results demonstrate
that our approach can deliver two orders of magnitude in reduction while still
keeping the errors of primary data under scientifically acceptable bounds.
Compared to reduction-based approaches based on SZ, our method achieves a
substantially higher compression ratio for a given error bound or a better
error for a given compression ratio.
