CBMS Regional Conference in the Mathematical Sciences--Recent Advances in the Numerical Approximation of Stochastic Partial Differential Equations

随机偏微分方程是许多随机影响系统的合适模型，中国科学技术大学学报（自然科学版），2010年8月。为了量化这些系统的不确定性和估计可预测性，随机偏微分方程的数值逼近是至关重要的。本次会议将由Peter E. Kloeden担任首席讲师，重点讨论模拟和分析随机偏微分方程的数值方法的新进展和可能的未来研究方向，以及在理解不确定性量化，噪声诱导现象和可预测性方面的应用。

NSF CBMS Regional Conference in the Mathematical Sciences:Recent Advances in the Numerical Approximation of Stochastic Partial Differential EquationsIllinois Institute of Technology, Chicago, August 2010Stochastic partial differential equations are appropriate models for many randomly influenced systems. In order to quantify uncertainty and estimate predictability of these systems, numerical approximations of stochastic partial differential equations are crucial.With Peter E. Kloeden as the Principal Lecturer, this conference will focus on new advances and possible future research directions in numerical approaches for simulating and analyzing stochastic partial differential equations, with applications for understanding uncertainty quantification, noise-induced phenomena and predictability.