NSTL国家科技图书文献中心

1. Stochastic analysis of FG-CNTRC conical shell panels based on a perturbation stochastic meshless method without partial derivative NSTL国家科技图书文献中心

Ping Xiang | Yufei Chen ... - 《Advances in engineering software》 - 2025,201(Mar.) - 103832.1～103832.18 - 共18页

摘要： the modified perturbation stochastic* method (MPSM* | into the modified perturbation stochastic* method and* | the Reproducing Kernel Particle Method* (RKPM) to* | calculate the first two-order estimates of the stochastic | are analyzed, and the corresponding stochastic* bands*

关键词： Random field | Modified perturbation stochastic method | FG-CNTRC | Conical shell panel

2. A momentum accelerated stochastic method and its application on policy search problems NSTL国家科技图书文献中心

Boou,Jiang | Ya-xiang,Yuan - 《Neural computing & applications》 - 2025,37(8) - 5957～5973 - 共17页

摘要：, researches on the first-order stochastic* methods and its* | reinforcement learning, we propose an accelerated stochastic | policy gradient method* with restart technique and* | With the dramatic increase in model complexity | and problem scales in the machine learning area

关键词： Stochastic algorithm | Non-convex optimization | Reinforcement learning

3. A Stochastic iteratively regularized Gauss-Newton method NSTL国家科技图书文献中心

Bergou, Elhoucine | Chada, Neil K.... - 《Inverse Problems》 - 2025,41(1) - 共21页

摘要： a stochastic* version of a wellknown inverse* | iteratively regularized Gauss-Newton method, originally | problems. Recent work have extended this method* to handle* | methods to a stochastic* framework through mini-batching* | , introducing a new algorithm, the stochastic* iteratively*

关键词： stochastic optimization | inverse problems | regularization | Gauss-Newton method | convergence analysis | random projection

4. Convergence of a stochastic variance reduced Levenberg-Marquardt method NSTL国家科技图书文献中心

Shao, Weiyi | Fan, Jinyan - 《Computational optimization and applications》 - 2025,90(2) - 417～444 - 共28页

摘要： stochastic variance reduced Levenberg-Marquardt method is | appropriately. Moreover, the method* converges to a stationary* | In this paper, we study the empirical residual | optimization problem with a least squares loss function. A | proposed for solving it. It is shown that both the

关键词： Least squares problems | Empirical residual optimization problem | Stochastic Levenberg-Marquardt algorithm | Variance reduced method | Mini-batch estimation

5. A multiscale stochastic particle method based on the Fokker-Planck model for nonequilibrium gas flows NSTL国家科技图书文献中心

Cui, Ziqi | Feng, Kaikai ... - 《Journal of Computational Physics》 - 2025,520 - 共25页

摘要： Boltzmann equation, the stochastic* particle method based* | Stochastic Particle (MSP) method. The MSP method quantifies | intermolecular collisions as continuous stochastic* processes in* | Carlo (DSMC) method, which is grounded in the | on the FP model, referred to as the SP-FP method

关键词： Nonequilibrium flow | Stochastic particle method | Fokker-Planck model | Multiscale modelling

6. A new numerical algorithm based on least squares method for solving stochastic Itô-Volterra integral equations NSTL国家科技图书文献中心

Zhang, Xueli | Huang, Jin ... - 《Numerical algorithms》 - 2025,98(1) - 117～132 - 共16页

摘要：In conjunction with least squares method* and* | stochastic Ito-Volterra integral equations. Firstly, the | , throughout this paper, stochastic* Ito integrals are* | accuracy of our proposed method. And in comparison with | method, the error of our presented approach is smaller.

关键词： Stochastic Ito-Volterra integral equations | Brownian motion process | Least squares method | Generalized hat functions

7. Weak convergence of the split-step backward Euler method for stochastic delay integro-differential equations NSTL国家科技图书文献中心

Yan Li | Qiuhong Xu ... - 《Mathematics and computers in simulation》 - 2025,227(Jan.) - 226～240 - 共15页

摘要： Euler (SSBE) method, renowned for its exceptional | stability when used to solve a class of stochastic* delay* | method used for solving the original SDIDEs and the | Euler-Maruyama method* applied to modified equations* | of the SSBE method* for SDIDEs. Finally, we present*

关键词： Stochastic delay integro-differential equations | Weak convergence | Split-step backward Euler method | Modified equation

8. Variance-based stochastic projection gradient method for two-stage co-coercive stochastic variational inequalities NSTL国家科技图书文献中心

Zhou, Bin | Jiang, Jie ... - 《Numerical algorithms》 - 2025,98(1) - 1～33 - 共33页 - 被引量：1

摘要： stochastic projection gradient method (DS-SPGM) for solving | The existing stochastic* approximation (SA* | )-type algorithms for two-stage stochastic* variational* | inequalities (SVIs) are based on the uniqueness of the second | -stage solution, which restricts the use of those

关键词： Two-stage stochastic variational inequalities | Stochastic approximation | Dynamic sampling | Co-coercivity

9. A novel stochastic power flow calculation and optimal control method for microgrid based on multivariate stochastic factors fusion - Sensitivity NSTL国家科技图书文献中心

Shi, Hongtao | Zhu, Jiahao ... - 《Measurement and Control》 - 2025,58(2) - 168～184 - 共17页

摘要： stochastic power flow calculation and optimal control | method for the microgrid based on multivariate | stochastic factors fusion-sensitivity (MSFF-sensitivity) is | stochastic factors fusion (MSFF) function is developed by | stochastic factors in the microgrid, which are effectively

关键词： Microgrid | multivariate normal distribution | stochastic power flow calculation | sensitivity analysis | voltage quality

10. Stochastic Geometric Iterative Method for Loop Subdivision Surface Fitting NSTL国家科技图书文献中心

Xu, Chenkai | He, Yaqi ... - 《Communications in Mathematics and Statistics》 - 2025,13(1) - 217～231 - 共15页

摘要：In this paper, we propose a stochastic | geometric iterative method* (S-GIM) to approximate the high* | algorithm. Then, our method* adjusts the control mesh* | -resolution 3D models by finite loop subdivision surfaces | . Given an input mesh as the fitting target, the initial

摘要： the modified perturbation stochastic* method (MPSM* | into the modified perturbation stochastic* method and* | the Reproducing Kernel Particle Method* (RKPM) to* | calculate the first two-order estimates of the stochastic | are analyzed, and the corresponding stochastic* bands*

关键词： Random field | Modified perturbation stochastic method | FG-CNTRC | Conical shell panel

摘要：, researches on the first-order stochastic* methods and its* | reinforcement learning, we propose an accelerated stochastic | policy gradient method* with restart technique and* | With the dramatic increase in model complexity | and problem scales in the machine learning area

关键词： Stochastic algorithm | Non-convex optimization | Reinforcement learning

摘要： a stochastic* version of a wellknown inverse* | iteratively regularized Gauss-Newton method, originally | problems. Recent work have extended this method* to handle* | methods to a stochastic* framework through mini-batching* | , introducing a new algorithm, the stochastic* iteratively*

关键词： stochastic optimization | inverse problems | regularization | Gauss-Newton method | convergence analysis | random projection

摘要： stochastic variance reduced Levenberg-Marquardt method is | appropriately. Moreover, the method* converges to a stationary* | In this paper, we study the empirical residual | optimization problem with a least squares loss function. A | proposed for solving it. It is shown that both the

关键词： Least squares problems | Empirical residual optimization problem | Stochastic Levenberg-Marquardt algorithm | Variance reduced method | Mini-batch estimation

摘要： Boltzmann equation, the stochastic* particle method based* | Stochastic Particle (MSP) method. The MSP method quantifies | intermolecular collisions as continuous stochastic* processes in* | Carlo (DSMC) method, which is grounded in the | on the FP model, referred to as the SP-FP method

关键词： Nonequilibrium flow | Stochastic particle method | Fokker-Planck model | Multiscale modelling

摘要：In conjunction with least squares method* and* | stochastic Ito-Volterra integral equations. Firstly, the | , throughout this paper, stochastic* Ito integrals are* | accuracy of our proposed method. And in comparison with | method, the error of our presented approach is smaller.

关键词： Stochastic Ito-Volterra integral equations | Brownian motion process | Least squares method | Generalized hat functions

摘要： Euler (SSBE) method, renowned for its exceptional | stability when used to solve a class of stochastic* delay* | method used for solving the original SDIDEs and the | Euler-Maruyama method* applied to modified equations* | of the SSBE method* for SDIDEs. Finally, we present*

关键词： Stochastic delay integro-differential equations | Weak convergence | Split-step backward Euler method | Modified equation

摘要： stochastic projection gradient method (DS-SPGM) for solving | The existing stochastic* approximation (SA* | )-type algorithms for two-stage stochastic* variational* | inequalities (SVIs) are based on the uniqueness of the second | -stage solution, which restricts the use of those

关键词： Two-stage stochastic variational inequalities | Stochastic approximation | Dynamic sampling | Co-coercivity

摘要： stochastic power flow calculation and optimal control | method for the microgrid based on multivariate | stochastic factors fusion-sensitivity (MSFF-sensitivity) is | stochastic factors fusion (MSFF) function is developed by | stochastic factors in the microgrid, which are effectively

关键词： Microgrid | multivariate normal distribution | stochastic power flow calculation | sensitivity analysis | voltage quality

摘要：In this paper, we propose a stochastic | geometric iterative method* (S-GIM) to approximate the high* | algorithm. Then, our method* adjusts the control mesh* | -resolution 3D models by finite loop subdivision surfaces | . Given an input mesh as the fitting target, the initial

关键词： Geometric iterative | Surface fitting | Subdivision surface | Stochastic PIA | Stochastic LSPIA

4008-161-200 800-990-8900

国家科技图书文献中心

摘要： the modified perturbation stochastic method (MPSM | into the modified perturbation stochastic method and | the Reproducing Kernel Particle Method (RKPM) to | calculate the first two-order estimates of the stochastic | are analyzed, and the corresponding stochastic bands

关键词： Random field | Modified perturbation stochastic method | FG-CNTRC | Conical shell panel

摘要：, researches on the first-order stochastic methods and its | reinforcement learning, we propose an accelerated stochastic | policy gradient method with restart technique and | With the dramatic increase in model complexity | and problem scales in the machine learning area

关键词： Stochastic algorithm | Non-convex optimization | Reinforcement learning

摘要： a stochastic version of a wellknown inverse | iteratively regularized Gauss-Newton method, originally | problems. Recent work have extended this method to handle | methods to a stochastic framework through mini-batching | , introducing a new algorithm, the stochastic iteratively

关键词： stochastic optimization | inverse problems | regularization | Gauss-Newton method | convergence analysis | random projection

摘要： stochastic variance reduced Levenberg-Marquardt method is | appropriately. Moreover, the method converges to a stationary | In this paper, we study the empirical residual | optimization problem with a least squares loss function. A | proposed for solving it. It is shown that both the

关键词： Least squares problems | Empirical residual optimization problem | Stochastic Levenberg-Marquardt algorithm | Variance reduced method | Mini-batch estimation

摘要： Boltzmann equation, the stochastic particle method based | Stochastic Particle (MSP) method. The MSP method quantifies | intermolecular collisions as continuous stochastic processes in | Carlo (DSMC) method, which is grounded in the | on the FP model, referred to as the SP-FP method

关键词： Nonequilibrium flow | Stochastic particle method | Fokker-Planck model | Multiscale modelling

摘要：In conjunction with least squares method and | stochastic Ito-Volterra integral equations. Firstly, the | , throughout this paper, stochastic Ito integrals are | accuracy of our proposed method. And in comparison with | method, the error of our presented approach is smaller.

关键词： Stochastic Ito-Volterra integral equations | Brownian motion process | Least squares method | Generalized hat functions

摘要： Euler (SSBE) method, renowned for its exceptional | stability when used to solve a class of stochastic delay | method used for solving the original SDIDEs and the | Euler-Maruyama method applied to modified equations | of the SSBE method for SDIDEs. Finally, we present

关键词： Stochastic delay integro-differential equations | Weak convergence | Split-step backward Euler method | Modified equation

摘要： stochastic projection gradient method (DS-SPGM) for solving | The existing stochastic approximation (SA | )-type algorithms for two-stage stochastic variational | inequalities (SVIs) are based on the uniqueness of the second | -stage solution, which restricts the use of those

关键词： Two-stage stochastic variational inequalities | Stochastic approximation | Dynamic sampling | Co-coercivity

摘要： stochastic power flow calculation and optimal control | method for the microgrid based on multivariate | stochastic factors fusion-sensitivity (MSFF-sensitivity) is | stochastic factors fusion (MSFF) function is developed by | stochastic factors in the microgrid, which are effectively

关键词： Microgrid | multivariate normal distribution | stochastic power flow calculation | sensitivity analysis | voltage quality

摘要：In this paper, we propose a stochastic | geometric iterative method (S-GIM) to approximate the high | algorithm. Then, our method adjusts the control mesh | -resolution 3D models by finite loop subdivision surfaces | . Given an input mesh as the fitting target, the initial

关键词： Geometric iterative | Surface fitting | Subdivision surface | Stochastic PIA | Stochastic LSPIA

4008-161-200 800-990-8900

国家科技图书文献中心

摘要： the modified perturbation stochastic* method (MPSM* | into the modified perturbation stochastic* method and* | the Reproducing Kernel Particle Method* (RKPM) to* | calculate the first two-order estimates of the stochastic | are analyzed, and the corresponding stochastic* bands*

摘要：, researches on the first-order stochastic* methods and its* | reinforcement learning, we propose an accelerated stochastic | policy gradient method* with restart technique and* | With the dramatic increase in model complexity | and problem scales in the machine learning area

摘要： a stochastic* version of a wellknown inverse* | iteratively regularized Gauss-Newton method, originally | problems. Recent work have extended this method* to handle* | methods to a stochastic* framework through mini-batching* | , introducing a new algorithm, the stochastic* iteratively*

摘要： stochastic variance reduced Levenberg-Marquardt method is | appropriately. Moreover, the method* converges to a stationary* | In this paper, we study the empirical residual | optimization problem with a least squares loss function. A | proposed for solving it. It is shown that both the

摘要： Boltzmann equation, the stochastic* particle method based* | Stochastic Particle (MSP) method. The MSP method quantifies | intermolecular collisions as continuous stochastic* processes in* | Carlo (DSMC) method, which is grounded in the | on the FP model, referred to as the SP-FP method

摘要：In conjunction with least squares method* and* | stochastic Ito-Volterra integral equations. Firstly, the | , throughout this paper, stochastic* Ito integrals are* | accuracy of our proposed method. And in comparison with | method, the error of our presented approach is smaller.

摘要： Euler (SSBE) method, renowned for its exceptional | stability when used to solve a class of stochastic* delay* | method used for solving the original SDIDEs and the | Euler-Maruyama method* applied to modified equations* | of the SSBE method* for SDIDEs. Finally, we present*

摘要： stochastic projection gradient method (DS-SPGM) for solving | The existing stochastic* approximation (SA* | )-type algorithms for two-stage stochastic* variational* | inequalities (SVIs) are based on the uniqueness of the second | -stage solution, which restricts the use of those

摘要：In this paper, we propose a stochastic | geometric iterative method* (S-GIM) to approximate the high* | algorithm. Then, our method* adjusts the control mesh* | -resolution 3D models by finite loop subdivision surfaces | . Given an input mesh as the fitting target, the initial