用物理规律约束深度学习，提升燃料密度预测精度与稳定性。
Physics-guided spatiotemporal neural models for fuel density prediction
- 将质量守恒和火势蔓延速率融入损失函数，引入物理约束
- 三种模型在多轮实验中均优于纯数据驱动基线，提升准确率与稳定性
- 适合需要物理合理性的火灾预测与林火管理决策场景
本文提出一种物理引导机器学习（PGML）框架，用于燃料密度预测，通过将物理约束和领域知识融入深度学习模型，提升模型的准确性与稳定性。研究对比了三种深度学习架构：ConvLSTM、自适应傅里叶神经算子（AFNONet）和视频视觉变压器（ViViT），用于建模燃料密度的时空演化。方法在损失函数中引入可微分的物理信息项，包括质量守恒的燃料输运项和火势蔓延速率估计。在多个独立实验中的平均结果表明，所提框架在准确性和稳定性上均优于无物理约束的纯数据驱动基线。该框架实现了计算高效、物理合理的火灾预测，有助于支持自适应计划烧除管理。
原文摘要 · Abstract (English)
This paper presents a physics-guided machine learning (PGML) framework for fuel density prediction, integrating physics constraints and domain knowledge into deep learning models to enhance model accuracy and stability. We explore three deep learning architectures -- ConvLSTM, Adaptive Fourier Neural Operator (AFNONet), and Video Vision Transformer (ViViT) -- to model the spatiotemporal evolution of fuel density. Our approach incorporates differentiable physics-informed terms in the loss function, including a mass-conserving fuel transport term and a rate-of-spread estimation. Experimental results, averaged across multiple independent trials, demonstrate that the proposed PGML framework outperforms purely data-driven baselines without physics constraints in both accuracy and stability. This framework enables computationally efficient, physically plausible fire forecasting to support adaptive prescribed burn management.
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