SPDEs

Statistical Models and Methods Based on Stochastic Partial Differential Equations

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This talk presents an overview of our research on statistical methods using stochastic partial differential equations (SPDEs), focusing on non-Gaussian random fields and fractional-order SPDEs, and theory for random fields and statistical analysis on metric graphs, highlighting theoretical contributions, software development, and applications relevant to KAUST RDI pillars.

Statistical models based on stochastic partial differential equations

Prof. David Bolin, Statistics, KAUST

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B9 L2 H1 R2322

The talk will give an overview of some recent developments of statistical models based on stochastic partial differential equations. We will in particular focus on equations with non-local differential operators or non-Gaussian driving noise, and explain when any why such models are useful. As motivating applications, analysis of longitudinal medical data and ocean waves will be considered.

Stochastic Partial Differential Equations (SPDEs)

1 min read ·
Mathematical models are widely used in physics and engineering applications as predictive tools. However, in many situations, the input parameters of the model are uncertain due to either a lack of knowledge or an intrinsic variability of the system. Examples are the study of subsurface phenomena, biological tissues, complex materials, whose properties are often heterogeneous, not perfectly characterized and, possibly, changing in time in an uncertain way. In this line of research we consider the case in which the uncertainty can be described reasonably well in a probabilistic setting and we