14 citations · 16 across the 6 of their papers we have counts for
7 papers
Feature preserving data assimilation via feature alignment
Amit N. Subrahmanya, Adrian Sandu
Data assimilation combines information from physical observations and numerical simulation results to obtain better estimates of the state and parameters of a physical system. A wi…
EPIRK-W and EPIRK-K time discretization methods
Mahesh Narayanamurthi, Paul Tranquilli, Adrian Sandu +1
Exponential integrators are special time discretization methods where the traditional linear system solves used by implicit schemes are replaced with computing the action of matrix…
LIRK-W: Linearly-implicit Runge-Kutta methods with approximate matrix factorization
Paul Tranquilli, Adrian Sandu, Hong Zhang
This paper develops a new class of linearly implicit time integration schemes called Linearly-Implicit Runge-Kutta-W (LIRK-W) methods. These schemes are based on an implicit-explic…
A Hybrid Monte-Carlo Sampling Smoother for Four Dimensional Data Assimilation
Ahmed Attia, Vishwas Rao, Adrian Sandu
This paper constructs an ensemble-based sampling smoother for four-dimensional data assimilation using a Hybrid/Hamiltonian Monte-Carlo approach. The smoother samples efficiently f…
Application of approximate matrix factorization to high order linearly implicit Runge-Kutta methods
Hong Zhang, Adrian Sandu, Paul Tranquilli
Linearly implicit Runge-Kutta methods with approximate matrix factorization can solve efficiently large systems of differential equations that have a stiff linear part, e.g. reacti…
A Derivative-Free Trust Region Framework for Variational Data Assimilation
Elias D. Nino, Adrian Sandu
This study develops a hybrid ensemble-variational approach for solving data assimilation problems. The method, called TR-4D-EnKF, is based on a trust region framework and consists…
Exponential-Krylov methods for ordinary differential equations
Paul Tranquilli, Adrian Sandu
This paper develops a new class of exponential-type integrators where all the matrix exponentiations are performed in a single Krylov space of low dimension. The new family, called…