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Title:
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Computational aspects of the Stochastic Finite Element Method 

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Organizers:
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Manolis Papadrakakis
Nat. Techn. Univ. Athens
Greece
mpapadra@central.ntua.gr

Vissarion Papadopoulos
Nat. Techn. Univ. Athens
Greece
vpapado@central.ntua.gr

Dimos C. Charmpis
University of Cyprus 
Cyprus
charmpis@ucy.ac.cy

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Minisymposium description:
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The analysis of stochastic systems with material/geometric properties modelled by random fields 
has been the subject of extensive research in the past two decades. The majority of the work has 
been focused on developing Stochastic Finite Element Methodologies (SFEM) for the numerical solution 
of the stochastic differential equations involved. The most widely used SFEM approaches are 
approximate expansion/perturbation based methods. Although such methods have proven to be highly 
accurate and computationally efficient for a wide range of problems, there are still several classes 
of problems in stochastic mechanics involving combinations of strong nonlinearities, large variations 
of system properties and non-Gaussian system properties that can be solved with reasonable accuracy 
only through a computationally expensive Monte Carlo simulation approach. Therefore, the efficient 
computational treatment of such problems is of paramount importance in large-scale, real-world 
applications incorporating probabilistic/ stochastic concepts.

This Minisymposium on ?Computational Aspects of the Stochastic Finite Element Method?, which is 
organized in the framework of the USNCCM-9 Conference, aims at presenting recent advances in the field 
of large scale computations for general stochastic systems involving linear and/or nonlinear, static 
and/or dynamic behaviour. In this respect, the Minisymposium is concerned with computational 
methodologies for treating the aforementioned problems (simulation procedures, perturbation methods, 
approximate solutions, etc.), efficient algorithms to accelerate the solution of the resulting systems 
of equations (solution methods, preconditioning techniques, parallel processing schemes, etc.), methods 
for improving the efficiency of the Monte Carlo Simulation by reducing the required sampling size as 
well as engineering applications involving large-scale SFEM analysis in general.