数学学科Seminar第3040讲 适当正交分解外推方法的应用

创建时间:  2026/05/15  邵奋芬   浏览次数:   返回

报告题目 (Title):Applications of Proper Orthogonal Decomposition Extrapolation Methods(适当正交分解外推方法的应用)

报告人 (Speaker):李宏 教授(内蒙古大学)

报告时间 (Time):2026年5月18日(周一) 15:00

报告地点 (Place):校本部GJ303

邀请人(Inviter):盛万成

主办部门:理学院数学系

报告摘要:The applications of proper orthogonal decomposition (POD) extrapolation methods for numerically solving partial differential equations are discussed in the presentation. The core strategy constructs a low-dimensional POD basis from a few full-order solution snapshots on a short initial interval [0,T_0] (T_0\ll T), then extrapolates reduced-order solutions on [T_0,T] to avoid redundant computations. The reduced order method based on POD techniques, for example, the reduced order FEM for Burgers equation (coefficient vector reduction),POD-TDG-STFE for parabolic problems (space-time reduction with Radau quadrature), ROLGE for the Allen-Cahn equation, and ROLGE for the Cahn-Hilliard equation (both using SAV approaches with Legendre-Galerkin discretization). Across all methods, the error has a unified structure--classical discretization error plus a POD truncation term controlled by the number of basis functions $d$. Computational speedup ranges from $3\times$ to over $40\times$ with only $d=2$--$15$ basis functions, while preserving key properties such as energy stability for phase field models

上一条:数学学科Seminar第3041讲 长程系统的机器学习原子间势

下一条:数学学科Seminar第3039讲 具阻尼波方程稳定性


数学学科Seminar第3040讲 适当正交分解外推方法的应用

创建时间:  2026/05/15  邵奋芬   浏览次数:   返回

报告题目 (Title):Applications of Proper Orthogonal Decomposition Extrapolation Methods(适当正交分解外推方法的应用)

报告人 (Speaker):李宏 教授(内蒙古大学)

报告时间 (Time):2026年5月18日(周一) 15:00

报告地点 (Place):校本部GJ303

邀请人(Inviter):盛万成

主办部门:理学院数学系

报告摘要:The applications of proper orthogonal decomposition (POD) extrapolation methods for numerically solving partial differential equations are discussed in the presentation. The core strategy constructs a low-dimensional POD basis from a few full-order solution snapshots on a short initial interval [0,T_0] (T_0\ll T), then extrapolates reduced-order solutions on [T_0,T] to avoid redundant computations. The reduced order method based on POD techniques, for example, the reduced order FEM for Burgers equation (coefficient vector reduction),POD-TDG-STFE for parabolic problems (space-time reduction with Radau quadrature), ROLGE for the Allen-Cahn equation, and ROLGE for the Cahn-Hilliard equation (both using SAV approaches with Legendre-Galerkin discretization). Across all methods, the error has a unified structure--classical discretization error plus a POD truncation term controlled by the number of basis functions $d$. Computational speedup ranges from $3\times$ to over $40\times$ with only $d=2$--$15$ basis functions, while preserving key properties such as energy stability for phase field models

上一条:数学学科Seminar第3041讲 长程系统的机器学习原子间势

下一条:数学学科Seminar第3039讲 具阻尼波方程稳定性