从稀疏数据中学习飞蛾幼虫的移动规律，提升害虫预测能力。
Weak Form Learning for Mean-Field Partial Differential Equations: an Application to Insect Movement
- 用弱形式方法结合核密度估计，从少量观测数据中推导运动方程。
- 在模拟农田环境中成功建模斜纹夜蛾幼虫的移动行为，数据稀疏但有效。
- 适合做农业害虫扩散预测与智能管理的研究者参考。
受感染、捕食及各向异性环境影响的昆虫物种可能表现出偏好性移动模式。由于短时间尺度上外部因素具有内在随机性，个体昆虫轨迹通常服从阻尼随机动力学。实际中，从观测的昆虫分布数据中学习其背后的福克-普朗克方程，是理解与预测此类行为的理想工具。掌握农林害虫的扩散动态有助于更准确地预测爆发强度与位置，从而改善害虫管理。本文将弱形式方程学习技术与核密度估计相结合，用于从高度稀疏的实验数据中学习鳞翅目幼虫种群移动的有效模型。近年来，如弱形式稀疏非线性动力学识别（WSINDy）等伽辽金方法已在多个科学领域证明其学习控制方程的有效性。我们在模拟农业条件下获取的斜纹夜蛾（Spodoptera frugiperda）幼虫位置测量稀疏数据集上验证了该方法的实用性，数据包含不同植物资源和感染状态的条件。
原文摘要 · Abstract (English)
Insect species subject to infection, predation, and anisotropic environmental conditions may exhibit preferential movement patterns. Given the innate stochasticity of exogenous factors driving these patterns over short timescales, individual insect trajectories typically obey overdamped stochastic dynamics. In practice, data-driven modeling approaches designed to learn the underlying Fokker-Planck equations from observed insect distributions serve as ideal tools for understanding and predicting such behavior. Understanding dispersal dynamics of crop and silvicultural pests can lead to a better forecasting of outbreak intensity and location, which can result in better pest management. In this work, we extend weak-form equation learning techniques, coupled with kernel density estimation, to learn effective models for lepidopteran larval population movement from highly sparse experimental data. Galerkin methods such as the Weak form Sparse Identification of Nonlinear Dynamics (WSINDy) algorithm have recently proven useful for learning governing equations in several scientific contexts. We demonstrate the utility of the method on a sparse dataset of position measurements of fall armyworms (Spodoptera frugiperda) obtained in simulated agricultural conditions with varied plant resources and infection status.
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