4 citations · 8 across the 9 of their papers we have counts for
9 papers
A Patankar predictor-corrector approach for positivity-preserving time integration
Kamila Nurkhametova, Reid J. Gomillion, Amit N. Subrahmanya +1
Many natural processes, such as chemical reactions and wave dynamics, are modeled as production-destruction (PD) systems that obey positivity and linear conservation laws. Classica…
Latent Twins
Matthias Chung, Deepanshu Verma, Max Collins +3
Over the past decade, scientific machine learning has transformed the development of mathematical and computational frameworks for analyzing, modeling, and predicting complex syste…
A copula-based rank histogram ensemble filter
Amit N. Subrahmanya, Julie Bessac, Andrey A. Popov +1
Serial ensemble filters implement triangular probability transport maps to reduce high-dimensional inference problems to sequences of state-by-state univariate inference problems.…
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…
Preserving Nonlinear Constraints in Variational Flow Filtering Data Assimilation
Amit N. Subrahmanya, Andrey A. Popov, Reid J. Gomillion +1
Data assimilation aims to estimate the states of a dynamical system by optimally combining sparse and noisy observations of the physical system with uncertain forecasts produced by…
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…
Ensemble Variational Fokker-Planck Methods for Data Assimilation
Amit N Subrahmanya, Andrey A Popov, Adrian Sandu
Particle flow filters solve Bayesian inference problems by smoothly transforming a set of particles into samples from the posterior distribution. Particles move in state space unde…
Adjoint-Matching Neural Network Surrogates for Fast 4D-Var Data Assimilation
Austin Chennault, Andrey A. Popov, Amit N. Subrahmanya +4
Data assimilation is the process of fusing information from imperfect computer simulations with noisy, sparse measurements of reality to obtain improved estimates of the state or p…
A Stochastic Covariance Shrinkage Approach to Particle Rejuvenation in the Ensemble Transform Particle Filter
Andrey A Popov, Amit N Subrahmanya, Adrian Sandu
Rejuvenation in particle filters is necessary to prevent the collapse of the weights when the number of particles is insufficient to sample the high probability regions of the stat…