数据驱动计算的变形快速傅里叶变换包络卡尔曼滤波在传染病传播模拟中的应用
Data Driven Computing by the Morphing Fast Fourier Transform Ensemble Kalman Filter in Epidemic Spread Simulations
摘要：
基于S-I-R传播模型，提出了FFT EnKF数据同化方法，并将其应用于流行病的随机细胞模拟。FFT EnKF将空间统计学和系综滤波方法组合成具有非常小的系综的EnKF的局部化和计算上廉价的版本，并且它进一步与变形EnKF组合以同化流行病位置的变化。
英文摘要：
The FFT EnKF data assimilation method is proposed and applied to a stochastic cell simulation of an epidemic, based on the S-I-R spread model. The FFT EnKF combines spatial statistics and ensemble filtering methodologies into a localized and computationally inexpensive version of EnKF with a very small ensemble, and it is further combined with the morphing EnKF to assimilate changes in the position of the epidemic.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
