1 citations · 1 across the 7 of their papers we have counts for
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Parametric Hierarchical Matrix Approximations to Kernel Matrices
Abraham Khan, Chao Chen, Vishwas Rao +1
Kernel matrices are ubiquitous in computational mathematics, often arising from applications in machine learning and scientific computing. In two or three spatial or feature dimens…
Structured Column Subset Selection for Bayesian Optimal Experimental Design
Hugo Díaz, Arvind K. Saibaba, Srinivas Eswar +2
We consider optimal experimental design (OED) for Bayesian inverse problems, where the experimental design variables have a certain multiway structure. Given different experime…
Optimal sensor placement under model uncertainty in the weak-constraint 4D-Var framework
Alen Alexanderian, Hugo Díaz, Vishwas Rao +1
In data assimilation, the model may be subject to uncertainties and errors. The weak-constraint data assimilation framework enables incorporating model uncertainty in the dynamics…
Bayesian D-Optimal Experimental Designs via Column Subset Selection
Srinivas Eswar, Vishwas Rao, Arvind K. Saibaba
This paper tackles optimal sensor placement for Bayesian linear inverse problems, a popular version of the more general Optimal Experimental Design (OED) problem, using the D-optim…
Randomized Preconditioned Solvers for Strong Constraint 4D-Var Data Assimilation
Amit N. Subrahmanya, Vishwas Rao, Arvind K. Saibaba
The Strong Constraint 4D Variational (SC-4DVAR) data assimilation method is widely used in climate and weather applications. SC-4DVAR involves solving a minimization problem to com…