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.

使用保证误差界限的保证块自编码器（GBATC）方法，通过张量相关性（Tensor Correlations）减少计算流体力学（CFD）等科学应用程序产生的时空数据。该方法使用一个多维张量块（跨越空间和时间）作为输入和输出，捕捉张量内的时空和物种间的关系。实验结果表明，该方法可以在保持原始数据误差在科学上可接受范围内，实现二个数量级的减少。与基于 SZ 的减少方案相比，我们的方法在给定误差界限下可以达到更高的压缩比或给定压缩比下拥有更好的误差。