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  • Sridharakumar Narasimhan

Sridharakumar Narasimhan

Performance Evaluation of ChaosFortress Lightweight Cryptographic Algorithm for Data Security in Water and Other Utility Management
IoT enabled smart water metering using multi sensor data and machine learning techniques
Rapid kinetic modelling of reaction systems from spectroscopic data in flow reactors
Continuous and discrete operation of water distribution networks with demand nodes at dissimilar heads
Economic Model Predictive Control for Water Distribution Networks
Experimental Validation of Graph Theory-Based Leak Detection Algorithm
Cloud-Based Control and Monitoring of Water Distribution Network Using Free Spectrum Communication Protocols
Economic Performance of Model Predictive Control at Back-off Operating Point
A Priori Parameter Identifiability of Enzymatic Reaction Networks
Semi-supervised machine learning approach for reaction stoichiometry and kinetic model identification using spectral data from flow reactors
Analysis of potential flow networks: Variations in transport time with discrete, continuous, and selfish operation
Equitable supply in intermittently operated rural water networks in emerging economies
Data Driven Monitoring of Water Distribution Networks
Online Data Analysis for Rapid Identification and Monitoring of Reaction Systems based on multi-sensor and multi-scale data
State Estimation for Non–linear Fully-implicit, Index-1 Differential Algebraic Equation Systems
A priori parameter identifiability in models with non-rational functions
Analysis of Experimental Conditions, Measurement Strategies, and Model Identification Approaches on Parameter Estimation in Plug Flow Reactors
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Founded in 2024, the School brings together several faculty with expertise in various areas of Data Science and AI, to work together on impactful problems of direct relevance to the society.

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