stochastic differential equations

Adaptive Multi Level Monte Carlo (MLMC)

Wed, Jun 1 2011 - Fri, Nov 1 2013

stochastic differential equations

Stochastic differential equations (SDEs), both ordinary time-dependent equations and partial differential equations with random coefficients, are common mathematical tools to model natural processes with uncertainty.

SDEs with irregular coefficients By Prof. Arturo Kohatsu (Ritsumeikan University, Japan)

Prof. Arturo Kohatsu, Ritsumeikan University, Japan

Mar 27, 14:00 - 15:00

B1 R4214

stochastic differential equations

In this talk we will give some results regarding the existence and regularity of densities for stochastic differential equations (sde's) with irregular coefficients starting with Holder coefficients and then bounded and measurable. If time allows we will also discuss issues related to the simulation schemes for such sde's.

Efficient Numerical Methods for Stochastic Differential Equations in Computational Finance by Juho Häppölä

Juho Häppölä, Ph.D., Applied Mathematics and Computational Sciences

Aug 24, 15:00 - 16:30

B3 R5209

stochastic differential equations

Stochastic Differential Equations (SDE) offer a rich framework to model the probabilistic evolution of the state of a system. Numerical approximation methods are typically needed in evaluating relevant Quantities of Interest arising from such models. In this dissertation, we present novel effective methods for evaluating Quantities of Interest relevant to computational finance when the state of the system is described by an SDE.

Raul Tempone

Professor, Applied Mathematics and Computational Sciences

numerical analysis Computational finance computational statistics stochastic differential equations uncertainty quantification bayesian inference data assimilation hierarchical and sparse approximation optimal control optimal experimental design stochastic optimization

Professor Raul Tempone is an worlwide recognized researcher in numerical analysis and uncertainty quantification. Professor Raul Tempone at KAUST leads the stochastic numerics research group, significantly contributing to Saudi Arabia's Vision 2030 goals through advancements in computational science. His work in adaptive algorithms, Bayesian inverse problems, and scientific machine learning drives forward critical applications in technology and sustainability, embodying KAUST's commitment to global scientific leadership and economic diversification.