Optimized time-stepping methods for the numerical solution of differential equations

微分方程数值求解的优化时间步长方法

• 批准号: 228090-2008
• 负责人: Spiteri, Raymond
• 金额: $ 1.82万
• 依托单位: University of Saskatchewan
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2012
• 资助国家: 加拿大
• 起止时间: 2012-01-01 至 2013-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=503409
• 关键词: Optimized; time; stepping; methods; numerical

Evolutionary partial differential equations (PDEs) are used to mathematically model many important physical processes, ranging from to the interactions of sub-atomic particles to the motions of galaxies. These and other real-life problems are complex. Their solutions can only be simulated using a computer. However, a major advantage of using simulations in scientific discovery is the ability to generate data and analyze complex systems "in silico". This is particularly useful when real experiments are infeasible to carry out.

演化偏微分方程（PDEs）被用来对许多重要的物理过程进行数学建模，从亚原子粒子的相互作用到星系的运动。这些和其他现实生活中的问题都很复杂。他们的解决方案只能用计算机模拟。然而，在科学发现中使用模拟的一个主要优势是能够生成数据并“在计算机上”分析复杂系统。这在实际实验无法进行时特别有用。