A
Amarjit Budhiraja
Researcher at University of North Carolina at Chapel Hill
Publications - 184
Citations - 3361
Amarjit Budhiraja is an academic researcher from University of North Carolina at Chapel Hill. The author has contributed to research in topics: Rate function & Brownian motion. The author has an hindex of 28, co-authored 176 publications receiving 2961 citations. Previous affiliations of Amarjit Budhiraja include Brown University & Iowa State University.
Papers
More filters
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Large deviations for infinite dimensional stochastic dynamical systems
TL;DR: In this paper, a variational representation for functionals of Brownian motion is used to avoid large deviations analysis of solutions to stochastic differential equations and related processes, where the construction and justification of the approximations can be onerous.
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Large deviations for infinite dimensional stochastic dynamical systems
TL;DR: In this article, a variational representation for functionals of Brownian motion is used to avoid large deviations analysis of solutions to stochastic differential equations and related processes, where the construction and justification of the approximations can be onerous.
Journal ArticleDOI
Worst case propagated uncertainty of multidisciplinary systems in robust design optimization
TL;DR: In this paper, a robust optimization approach for estimating the worst case propagated uncertainty in multidisciplinary systems is developed and validated using Monte Carlo simulation in application to a small analytic problem and an Autonomous HoverCraft (AHC) problem.
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Variational representations for continuous time processes
TL;DR: In this article, a formule variationnelle for des fonctionnelles positives d'une mesure de Poisson aleatoire and d'un mouvement brownien is presented.
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A survey of numerical methods for nonlinear filtering problems
TL;DR: In this paper, a survey of particle filters for nonlinear filtering is presented, with a special emphasis on an important family of schemes known as the particle filters, and a numerical study is presented to illustrate that in settings where the signal/observation dynamics are non linear a suitably chosen nonlinear scheme can drastically outperform the extended Kalman filter.