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 improved scaling procedure for analysis and simplification of process models

Abstract: Systematic scaling analysis of model equations can be valuable as a tool for developing computationally tractable simulations of physical systems. The scaling analysis methods in literature pose difficulties in the calculation of scale and reference values, when nonlinear terms are involved in the model equations. Further, existing methods involve trial and error procedures in the scaling process. In this paper, a systematic approach for handling nonlinear terms is suggested, which results in appropriate scale and reference values that render the dimensionless variable variations to be of order one. Further, trial and error procedures are avoided through a new approach wherein a set of nonlinear algebraic equations are solved to identify the scale and reference values. The proposed scaling approach is common to any given model equations with fixed parameters. However, it is to be noted that the proposed procedure may not handle situations when model equations exhibit steady state multiplicity and have dynamic multi-mode regimes. The proposed scaling procedure is illustrated through various examples of different complexities. A 1D model of WGS reactor as a case study shows the effectiveness of the obtained scale and reference values in obtaining simplified model which represents the steady state and dynamic variations of the variables.

Computational framework for exploring the interplay of diet and gut microbiota in autism

Abstract: Autism spectrum disorder (ASD) refers to the set of complex neurological disorders characterized by repetitive behaviour The reported occurrence of abnormal gut bacteria, along with prevalence of gastrointestinal disorders in ASD indicate its strong correlation with the gut microflora Our study aims to understand the role of diet and gut bacteria in ASD via an integrated constraint-based and PBPK model Genome scale models of five major gut bacteria, which were reported to be associated with ASD, were integrated with the human host, ie, the combined small intestinal enterocyte and neuronal brain model Simultaneously, a permeability-limited two sub-compartment PBPK model was developed to determine the distribution of bacteria-derived toxins in the body The important results include, (i) inclusion of probiotics into the diet of autistic case restores gut balance, majorly seen as a result of reduced oxidative stress in the brain and the gut, (ii) microbiome and diet together mediate host metabolism in autism, majorly via the nucleotide, central carbon, amino acid, and reactive oxygen species metabolisms, and (iii) gut bacterial-specific secretions contribute to autistic metabotype Thus, the presented integrated model is the first ever quantitative model, providing a mechanistic basis for autism pathogenesis, capturing known biomarkers, as well as, highlighting the potential of novel dietary modifications in alleviating the symptoms of autism

Constraint Programming based Input Signal Design for System Identification

Abstract: The selection of appropriate input harmonics in a multi-harmonic signal for the identification of nonlinear systems leads to a nonlinear, combinatorial optimization problem. Lack of efficient optimization tools for solving such problems had previously led to the development of an explicit Integer Linear Programming (ILP) based lexicographic optimization formulation. However, the dimensionality of such a formulation increases considerably with an increase in the number of frequencies and the problem has been reported to become intractable for higher frequencies. In this article, we demonstrate the ability of Constraint Programming (CP) to efficiently model and solve the original nonlinear problem to guaranteed global optimality. We successfully demonstrate the ability of CP to determine the optimal solutions much faster and also solve problems that have so far remained intractable. In addition, we also show the ability of CP to determine all the multiple optimal solutions and near best optimal solutions in a single optimization run.

State and Parameter Estimation in Distributed Constrained Systems. 2. GA-EKF Based Sensor Placement for a Water Gas Shift Reactor

Abstract: Growing complexity of processes necessitates the use of information from sensors along with first-principles mathematical models to ensure safe and optimal operations. Use of sensors in complex processes requires identifying optimal location of sensors that can maximize information from a process. Classical sensor placement approaches for nonlinear systems that use state estimation schemes usually incorporate linearized models around the steady-state operating point. However, such approaches face difficulties when abnormalities or disturbances drift the system away from the normal operating point. Therefore, use of models that can appropriately track the behavior of the system in the sensor placement framework are of interest. However, the computational complexity of the detailed models makes such approaches intractable. In this work, we develop a sensor placement framework that combines genetic algorithms and the extended Kalman filter to obtain optimal sensor locations. Within this framework, we have in...

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.