In this paper, we propose a software component under Windows that generates pseudo random numbers using RDS （Refined Descriptive Sampling） as required by the simulation. RDS is regarded as the best sampling method a...In this paper, we propose a software component under Windows that generates pseudo random numbers using RDS （Refined Descriptive Sampling） as required by the simulation. RDS is regarded as the best sampling method as shown in the literature. In order to validate the proposed component, its implementation is proposed on approximating integrals. The simulation results from RDS using ＂RDSRnd＂ generator were compared to those obtained using the generator ＂Rnd＂ included in the Pascal programming language under Windows. The best results are given by the proposed software component.展开更多
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
文摘In this paper, we propose a software component under Windows that generates pseudo random numbers using RDS （Refined Descriptive Sampling） as required by the simulation. RDS is regarded as the best sampling method as shown in the literature. In order to validate the proposed component, its implementation is proposed on approximating integrals. The simulation results from RDS using ＂RDSRnd＂ generator were compared to those obtained using the generator ＂Rnd＂ included in the Pascal programming language under Windows. The best results are given by the proposed software component.
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).
