物理基础模型在适应过程中遗忘知识

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-02 05:00

一篇新研究论文探讨了物理基础模型 Walrus-1.3B 中的灾难性遗忘问题。研究发现，将该模型适应新数据（具体来说是模拟的圆柱体周围流动，然后是同一系统的实验数据）会显著降低其在原始预训练任务上的性能。在仅用模拟数据进行 20 个 epoch 的微调后，错误率最高增加了 35 倍。研究表明，在适应真实世界数据时重放一小部分原始模拟数据，可以在有效适应新实验动力学的同时，恢复大部分丢失的性能。 AI

影响强调了科学基础模型适应新数据时潜在的局限性，并提出重放策略作为缓解灾难性遗忘的方法。

排序理由这是一篇详细介绍特定模型行为发现的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Haaris Mian, Miles Cranmer, Alessandro Favero, Payel Mukhopadhyay · 2026-11-02 05:00

Frozen in Time: How a Physics Foundation Model Forgets Under Sim-to-Real Adaptation

arXiv:2610.40857v1 Announce Type: new Abstract: Scientific foundation models (SciFMs) have achieved remarkable performance on a wide range of PDEs, including the ability to efficiently adapt to unseen data distributions with additional finetuning. As with all neural architectures…

本文如何被排名

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Frozen in Time: How a Physics Foundation Model Forgets Under Sim-to-Real Adaptation

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