This study introduces a novel sequential data assimilation method that uses conditional denoising score matching(CDSM).The CDSM leverages iterative refinement of noisy samples guided by conditional score functions to ...This study introduces a novel sequential data assimilation method that uses conditional denoising score matching(CDSM).The CDSM leverages iterative refinement of noisy samples guided by conditional score functions to achieve real-time state estimation by incorporating observational constraints at each time step.Unlike traditional methods,such as variational assimilation and Kalman filtering,which rely on Gaussian assumptions and can be computationally expensive because of iterations or ensembles,CDSM is based on stochastic differential equations(SDEs).展开更多
基金supported by the National Natural Science Foundation of China(grant number 42450178).
文摘This study introduces a novel sequential data assimilation method that uses conditional denoising score matching(CDSM).The CDSM leverages iterative refinement of noisy samples guided by conditional score functions to achieve real-time state estimation by incorporating observational constraints at each time step.Unlike traditional methods,such as variational assimilation and Kalman filtering,which rely on Gaussian assumptions and can be computationally expensive because of iterations or ensembles,CDSM is based on stochastic differential equations(SDEs).
