arXiv subjects

Navaratnam Sri Namachchivaya

Publications and source records attributed to Navaratnam Sri Namachchivaya.

5 recordsLinked to original sources

arXiv2025

POD-Galerkin Reduced Order Modeling of the El Nino-Southern Oscillation (ENSO)

Reduced order modeling (ROM) aims to mitigate computational complexity by reducing the size of a high-dimensional state space. In this study, we demonstrate the efficiency, accuracy, and stability of proper orthogonal decomposition (POD)-Galerkin ROM when applied to the El Nino Southern Oscillation model, which integrates coupled atmosphere, ocean, and sea surface temperature (SST) mechanisms in the equatorial Pacific. While POD identifies the most energetic modes of a system from simulation data, the Galerkin projection maps the governing equations onto these reduced modes to derive a simplified dynamical system. Leveraging the unique coupling properties of the model, we propose a novel approach to formulate a reduced order model derived from Galerkin projection. Our approach achieves remarkable computational efficiency, requiring only four POD modes. The results provide highly stable and accurate solutions over 95% compared to the high-dimensional full-order model (FOM), highlighting the potential of POD-Galerkin reduction for efficient and accurate climate simulations.

physics.ao-ph↗

arXiv2021

Stochastic Parameterization using Compressed Sensing: Application to the Lorenz-96 Atmospheric Model

Growing set of optimization and regression techniques, based upon sparse representations of signals, to build models from data sets has received widespread attention recently with the advent of compressed sensing. This paper deals with the parameterization of the Lorenz-96 model with two time-scales that mimics mid-latitude atmospheric dynamics with microscopic convective processes. Compressed sensing is used to build models (vector fields) to emulate the behavior of the fine-scale process, so that explicit simulations become an online benchmark for parameterization. We apply compressed sensing, where the sparse recovery is achieved by constructing a sensing/dictionary matrix from ergodic samples generated by the Lorenz-96 atmospheric model, to parameterize the unresolved variables in terms of resolved variables. Stochastic parameterization is achieved by auto-regressive modelling of noise. We utilize the ensemble Kalman filter for data assimilation, where observations (direct measurements) are assimilated in the low-dimensional stochastic parameterized model to provide predictions. Finally, we compare the predictions of compressed sensing and Wilk's polynomial regression to demonstrate the potential effectiveness of the proposed methodology.

math.DS↗

arXiv2019

First Passage Time of Nonlinear Diffusion Processes with Singular Boundary Behavior

New theorems for the moments of the first passage time of one dimensional nonlinear stochastic processes with an entrance boundary are formulated. This important class of one dimensional stochastic processes results among others from approximations of the energy or amplitude of second order nonlinear stochastic differential equations. Since the diffusion of a stochastic process vanishes at an entrance boundary, the entrance boundary is called a singular point of the stochastic process. The theorems for the moments of the first passage times are validated based on existing analytical results. In addition, the first passage times of a nonlinear stochastic differential equation, which is important for the determination of dangerous ship roll dynamics, are calculated. The proposed analytical expressions for the moments of the first passage times can be calculated very fast using standard quadrature formulas.

math.AP↗

arXiv2017

Random Perturbations of a Periodically Driven Nonlinear Oscillator: Escape from a resonance zone

The phase space for a periodically driven nonlinear oscillator consists of many resonance zones. Let the strength of periodic excitation and the strength of the damping be indexed by a small parameter $\varepsilon$. It is well known that, as $\varepsilon \to 0$, the measure of the set of initial conditions which lead to 'capture in a resonance zone' goes to zero. In this paper we study the effect of weak noise on the escape from a resonance zone.

math.PR↗

arXiv2017

Exponentially-ergodic Markovian noise perturbations of delay differential equations at Hopf bifurcation

We consider noise perturbations of delay differential equations (DDE) experiencing Hopf bifurcation. The noise is assumed to be exponentially ergodic, i.e. transition density converges to stationary density exponentially fast uniformly in the initial condition. We show that, under an appropriate change of time scale, as the strength of the perturbations decreases to zero, the law of the critical eigenmodes converges to the law of a diffusion process (without delay). We prove the result only for scalar DDE. For vector-valued DDE without proofs see Phys.Rev.E v93, 062104.

math.PR↗