Raghunathan Rengaswamy

Bio: Raghunathan Rengaswamy is an academic researcher from Indian Institute of Technology Madras. The author has contributed to research in topics: Proton exchange membrane fuel cell & Fault detection and isolation. The author has an hindex of 39, co-authored 210 publications receiving 9632 citations. Previous affiliations of Raghunathan Rengaswamy include Indian Institute of Technology Bombay & Bosch.

An optimal strategy to model microbial growth in a multiple substrate environment.

Abstract: A comprehensive model is developed based on an optimal strategy describing varied microbial growth phenomenon involving sequential and simultaneous utilization of substrate. The model mimics the complex regulatory process of a cell which results in diverse growth process with the help of simple multi-variable constrained optimization, which aims at maximizing the specific cell growth. The metabolic processes of a cell are represented by simple flux balance equations. The different growth phenomenon exhibited by a microorganism are attributed to different levels of control present inside the cell. Provision is made in the model for these controls, in the form of constraints in the optimization formulation. The model prediction matches well with the experimental data of simultaneous growth of E. coli K12 on a mixture of glucose and organic acids like lactate, pyruvate, and acetate. Moreover, the model predictions are well in agreement with earlier published experimental data for the growth of E. coli K12 on other organic acids like fumarate, alpha-ketoglutarate, and succinate. (c) 1997 John Wiley & Sons, Inc. Biotechnol Bioeng 56: 635-644, 1997.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

The Ensemble Kalman Filter: Theoretical formulation and practical implementation

Abstract: The purpose of this paper is to provide a comprehensive presentation and interpretation of the Ensemble Kalman Filter (EnKF) and its numerical implementation. The EnKF has a large user group, and numerous publications have discussed applications and theoretical aspects of it. This paper reviews the important results from these studies and also presents new ideas and alternative interpretations which further explain the success of the EnKF. In addition to providing the theoretical framework needed for using the EnKF, there is also a focus on the algorithmic formulation and optimal numerical implementation. A program listing is given for some of the key subroutines. The paper also touches upon specific issues such as the use of nonlinear measurements, in situ profiles of temperature and salinity, and data which are available with high frequency in time. An ensemble based optimal interpolation (EnOI) scheme is presented as a cost-effective approach which may serve as an alternative to the EnKF in some applications. A fairly extensive discussion is devoted to the use of time correlated model errors and the estimation of model bias.