发表机构
Faculty of Physics and Astronomy, Ruhr University Bochum; Virtual Machining Group, TU Dortmund; Faculty of Physics（鲁尔大学波鸿分校物理与天文学学院; 多特蒙德工业大学虚拟加工组; 物理学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出PhysAttNet，一种物理信息注意力框架，通过三种物理引导的正则化改进轻量型CNN预测器，在铣削切削力和blazar耀斑预测任务中提升了时间序列预测的精度、泛化性及对关键事件的性能。
AI 中文摘要
准确且鲁棒的时间序列预测对于涉及物理过程的众多应用至关重要，例如制造监控和天体物理事件检测。在这些场景中，预测模型必须在噪声、变异性和测量不确定性下保持可靠，同时捕捉对应于物理意义事件的时间局部结构。卷积神经网络（CNN）因计算效率高和表征能力强而被广泛用于此类任务。然而，其学习到的时间表征常表现出不稳定或物理不一致的注意力模式，降低了鲁棒性、泛化性和可解释性。本文提出PhysAttNet，一种用于时间序列预测的物理信息注意力框架。PhysAttNet在轻量型CNN预测器的基础上，添加了一个由领域知识正则化引导的注意力头，该正则化反映了物理信号的结构特性。具体而言，训练过程中施加了三个互补约束：对齐正则化，鼓励注意力遵循源自输入信号的平滑、以峰值为中心的时间结构；平滑性正则化，强制连续的时间演化；稀疏性正则化，促进对信息区间的选择性关注。这些可微分正则化项引入了物理引导的归纳偏置，无需标注解释或人工监督。在两个不同应用上的实验，即铣削过程中切削力的预测和 blazar 时间序列中耀斑的预测，表明PhysAttNet提高了预测精度、泛化性和对结构重要事件的预测性能。
英文摘要
Accurate and robust time series forecasting is essential in many applications involving physical processes, such as manufacturing monitoring and astrophysical event detection. In these settings, predictive models must remain reliable under noise, variability, and measurement uncertainty while capturing temporally localized structures corresponding to physically meaningful events. Convolutional neural networks (CNNs) are widely used for such tasks due to their computational efficiency and strong representational capacity. However, their learned temporal representations often exhibit unstable or physically inconsistent attention patterns, reducing robustness, generalization, and interpretability. This paper introduces PhysAttNet, a physics-informed attention framework for time series forecasting. PhysAttNet augments a lightweight CNN forecaster with an attention head guided by domain-informed regularization reflecting the structural properties of physical signals. Specifically, three complementary constraints are imposed during training: an alignment regularization that encourages attention to follow smooth, peak-centered temporal structures derived from the input signal, a smoothness regularization that enforces continuous temporal evolution, and a sparsity regularization that promotes selective focus on informative intervals. These differentiable regularization terms introduce physics-guided inductive bias without requiring annotated explanations or manual supervision. Experiments on two distinct applications, namely predicting cutting forces during milling and forecasting flares in blazar time series, demonstrate that PhysAttNet improves forecasting accuracy, generalization, and prediction performance on structurally important events.
