English(EN) Lossy Compression of PDE Training Inputs: Field Reconstruction Error Does Not Order the Cost to a Trained Operator

研究：场重构误差未能预测PDE算子性能

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-07 04:00

一篇新的研究论文探讨了用于求解偏微分方程（PDE）的训练数据的有损压缩所面临的挑战。研究表明，诸如场重构误差等传统指标并不能准确预测在压缩数据上训练的算子的性能。相反，一种新颖的探测方法，通过测量压缩场在已训练算子中传递的扰动量，可以更一致地评估不同压缩编解码器和架构的数据集质量。 AI

影响这项研究可能导致更高效的科学机器学习大型数据集的存储和传输，从而可能降低成本并加速训练。

排序理由该集群包含一篇在arXiv上发表的研究论文，讨论了一种评估PDE算子压缩训练数据的新颖方法。[lever_c_demoted from research: ic=1 ai=1.0]

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本文如何被排名

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arXiv cs.LG TIER_1 English(EN) · Huy Hoang Le · 2026-10-07 04:00

偏微分方程训练输入的有损压缩：场重建误差不会影响训练算子的成本排序

arXiv:2610.06095v2 Announce Type: replace Abstract: Operator-learning benchmarks are stored at full precision and have grown to terabyte scale. Rate-distortion theory says how many bits the stored field needs, while a practitioner needs to know how accurate an operator trained on…