让大模型学会物理规律，提升预测真实性和泛化能力。
Towards Physics-Guided Foundation Models
- 在预训练中融入通用物理知识，约束模型输出
- 显著改善模型在分布外数据上的合理性与可行性
- 适合需要高可信度模拟的科研与工程场景
传统基础模型通过在广泛数据集上预训练，降低下游任务微调所需的资源（如时间、能耗、标注样本）。然而，这类模型在分布外预测时表现不佳，常生成不现实且违反物理规律的结果。本文提出物理引导的基础模型（PGFM），即在模型中集成适用于多种下游任务的广义物理知识（如科学规律）。该方法使模型在未知场景下仍能输出符合物理原理的合理结果，增强其可靠性与可解释性。
原文摘要 · Abstract (English)
Traditional foundation models are pre-trained on broad datasets to reduce the training resources (e.g., time, energy, labeled samples) needed for fine-tuning a wide range of downstream tasks. However, traditional foundation models struggle with out-of-distribution prediction and can produce outputs that are unrealistic and physically infeasible. We propose the notation of physics-guided foundation models (PGFM), that is, foundation models integrated with broad or general domain (e.g., scientific) physical knowledge applicable to a wide range of downstream tasks.
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