耗散知识动力学模型的可辨识性研究

1. 12:00
   arXiv · Statistics Machine Learning

   耗散知识动力学模型的可辨识性：设计激励下的精确恢复与观测数据上的退化

   研究将人类学习建模为含概念迁移矩阵、逐概念遗忘率和饱和练习响应增益的非线性耗散常微分方程系统，并证明在显式激励条件下参数可结构辨识，双概念情形给出闭式恢复。数值上提出半隐式 L-stable 格式与批量求解器，预测逐位精确、梯度达 $10^{-10}$，速度提升两个数量级，可支持 $10^5$ 学习者规模估计。

Current heat 9·Comparable peak 10（Oct 8）·Comparable change over 24 hours –

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