English(EN) HERO: History-Enriched Rollout Training for Long-Horizon Autoregressive Neural Operators

新的HERO训练方法提高了神经算子长时域的准确性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-03 04:00

研究人员引入了HERO（History-Enriched Rollout Training，历史增强回滚训练），一种旨在提高自回归神经算子长时域准确性的新方法。该技术解决了模型递归使用自身预测时误差累积的问题，这是模拟时间相关偏微分方程时的一个常见问题。HERO通过整合源自模型优化历史的相对监督来增强标准训练，使用一组排序的候选回滚来建立更具信息量的比较基线。在九个PDE基准上的实验表明，HERO在不增加推理时间的情况下，始终提高了长时域的准确性和鲁棒性。 AI

影响提高了科学模拟中自回归模型在长时域的准确性和鲁棒性。

排序理由该集群包含一篇详细介绍神经算子新训练方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Jiaquan Zhang, Shuxu Chen, Haifan Meng, Yi Lu, Zhihan Lyu, Fan Mo, Wei Dong, Yang Yang, Chaoning Zhang · 2026-08-03 04:00

HERO：面向长时域自回归神经算子的历史增强回滚训练

arXiv:2607.29135v1 Announce Type: cross Abstract: Neural operators provide fast surrogates for time-dependent partial differential equations (PDEs) by applying a learned evolution operator recursively to its own predictions, but this autoregressive rollout feeds every prediction …

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报道来源 [1]

HERO：面向长时域自回归神经算子的历史增强回滚训练

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