In this presentation,we will advocate the exploration of synergies between the machine learning and uncertainty quantification research communities towards
Efficient solution strategy for stochastic partial differential equations(SPDE)has been a classical topic,as many physical phenomena are inherently random.
In this talk,we discuss the stochastic collocation methods via l1 minimization,by randomly sampling from the corresponding tensor grid of Gaussian points.
We consider the problem faced by an economic agent trying to find the optimal strategies for the joint management of her consumption from a basket of K good
The minimum local fill(MLF)heuristic computes a fill-reducing permutation for sparse Cholesky factorization.It is generally believed that MLF is very expens
Efficient processing of many scientific applications that involve repeated sparse matrix computations requires reordering rows/columns of these matrices int
Sparse matrix computation is rich in combinatorial problems.Reordering for sparsity preservation in matrix factorization is one such problem.Partitioning fo
A common and convenient way to model multi-component phenomena is to model the components seperately and to glue them together via coupling the variables at
In the context of hybrid sparse linear solvers based on domain decomposition and Schur complement approaches,getting a domain decomposition tool leading to