# Mayank BaranwalIndian Institute of Technology Bombay | IIT Bombay · Department of Systems and Control Engineering

Mayank Baranwal

PhD - Mechanical Engineering

## About

66

Publications

28,247

Reads

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371

Citations

Citations since 2017

Introduction

Additional affiliations

August 2018 - present

May 2013 - August 2013

August 2011 - December 2014

Education

May 2015 - April 2018

August 2014 - May 2015

August 2011 - August 2014

## Publications

Publications (66)

The Vehicle Routing Problem with Time-Windows (VRPTW) is an important problem in allocating resources on networks in time and space. We present in this paper a Deterministic Annealing (DA)-based approach to solving the VRPTW with its aspects of routing and scheduling, as well as to model additional constraints of heterogeneous vehicles and shipment...

This paper presents a novel and efficient heuristic framework for approximating the solutions to the multiple traveling salesmen problem (m-TSP) and other variants on the TSP. The approach adopted in this paper is an extension of the Maximum-Entropy-Principle (MEP) and the Deterministic Annealing (DA) algorithm. The framework is presented as a gene...

This article presents a distributed, robust and optimal control architecture for a network of multiple DC-DC converters. The network of converters considered form a DC microgrid in order to regulate a desired DC bus voltage and meet prescribed time-varying power sharing criteria among different energy sources. Such coordinated microgrids provide an...

One of the main challenges in cluster analysis is estimating the true number of clusters in a dataset. This paper quantifies a notion of persistence of a clustering solution over a range of resolution scales, which is used to characterize the natural clusters and estimate the true number of clusters in a dataset. We show that this quantification of...

Preterm births (PTBs), i.e., births before 37 weeks of gestation are completed, are one of the leading issues concerning infant health, and is a problem that plagues all parts of the world. Millions of infants are born preterm globally each year, resulting in developmental disorders in infants and increase in neonatal mortality. Although there are...

Power grids, across the world, play an important societal and economical role by providing uninterrupted, reliable and transient-free power to several industries, businesses and household consumers. With the advent of renewable power resources and EVs resulting into uncertain generation and highly dynamic load demands, it has become ever so importa...

Gradient-based first-order convex optimization algorithms find widespread applicability in a variety of domains, including machine learning tasks. Motivated by the recent advances in fixed-time stability theory of continuous-time dynamical systems, we introduce a generalized framework for designing accelerated optimization algorithms with strongest...

Power grids, across the world, play an important societal and economical role by providing uninterrupted, reliable and transient-free power to several industries, businesses and household consumers. With the advent of renewable power resources and EVs resulting into uncertain generation and highly dynamic load demands, it has become ever so importa...

Background
Development of new methods for analysis of protein–protein interactions (PPIs) at molecular and nanometer scales gives insights into intracellular signaling pathways and will improve understanding of protein functions, as well as other nanoscale structures of biological and abiological origins. Recent advances in computational tools, par...

This study develops a fixed-time convergent saddle point dynamical system for solving min-max problems under a relaxation of standard convexity-concavity assumption. In particular, it is shown that by leveraging the dynamical systems viewpoint of an optimization algorithm, accelerated convergence to a saddle point can be obtained. Instead of requir...

We present a novel Maximum Entropy Principle (MEP)-based modeling and algorithmic approach, for a large class of routing and scheduling problems including the Capacitated Vehicle Routing Problem (CVRP), the Vehicle Routing Problem with soft time-windows (VRPTW) and the Close-Enough Traveling Salesman Problem (CETSP). The MEP models routing and sche...

Accelerated gradient methods are the cornerstones of large-scale, data-driven optimization problems that arise naturally in machine learning and other fields concerning data analysis. We introduce a gradient-based optimization framework for achieving acceleration, based on the recently introduced notion of fixed-time stability of dynamical systems....

Predicting the dynamics and functions of microbiomes constructed from the bottom-up is a key challenge in exploiting them to our benefit. Current models based on ecological theory fail to capture complex community behaviors due to higher order interactions, do not scale well with increasing complexity and in considering multiple functions. We devel...

Graph convolutional networks (GCNs) are a widely used method for graph representation learning. To elucidate their capabilities and limitations for graph classification, we investigate their power to generate well-separated embedding vectors for graphs sampled from different random graph models, which correspond to different class-conditional distr...

This paper presents three techniques for scheduling for crude transfer between a port and a refinery on a single pipeline in the presence of stringent flow constraints. The three techniques are based on metaheuristics (business rules), mixed integer linear programming and reinforcement learning. In addition to comparing the three techniques, we als...

Most existing literature on supply chain and inventory management consider stochastic demand processes with zero or constant lead times. While it is true that in certain niche scenarios, uncertainty in lead times can be ignored, most real-world scenarios exhibit stochasticity in lead times. These random fluctuations can be caused due to uncertainty...

In this paper, a novel modified proximal dynamical system is proposed to compute the solution of a mixed variational inequality problem (MVIP) within a fixed time, where the time of convergence is finite and is uniformly bounded for all initial conditions. Under the assumptions of strong monotonicity and Lipschitz continuity, it is shown that a sol...

Accelerated gradient methods are the cornerstones of large-scale, data-driven optimization problems that arise naturally in machine learning and other fields concerning data analysis. We introduce a gradient-based optimization framework for achieving acceleration, based on the recently introduced notion of fixed-time stability of dynamical systems....

Predicting the dynamics and functions of microbiomes constructed from the bottom-up is a key challenge in exploiting them to our benefit. Current ordinary differential equation-based models fail to capture complex behaviors that fall outside of a predetermined ecological theory and do not scale well with increasing community complexity and in consi...

Early detection of Pancreatic Ductal Adenocarcinoma (PDAC), one of the most aggressive malignancies of the pancreas, is crucial to avoid metastatic spread to other body regions. Detection of pancreatic cancer is typically carried out by assessing the distribution and arrangement of tumor and immune cells in histology images. This is further complic...

Several real-world scenarios, such as remote control and sensing, are comprised of action and observation delays. The presence of delays degrades the performance of reinforcement learning (RL) algorithms, often to such an extent that algorithms fail to learn anything substantial. This paper formally describes the notion of Markov Decision Processes...

Determining optimum inventory replenishment decisions are critical for retail businesses with uncertain demand. The problem becomes particularly challenging when multiple products with different lead times and cross-product constraints are considered. This paper addresses the aforementioned challenges in multi-product, multi-period inventory manage...

A wide variety of real life complex networks are prohibitively large for modeling, analysis and control. Understanding the structure and dynamics of such networks entails creating a smaller representative network that preserves its relevant topological and dynamical properties. While modern machine learning methods have enabled identification of go...

Development of new methods for analysis of protein-protein interactions (PPIs) at molecular and nanometer scales gives insights into intracellular signaling pathways and will improve understanding of protein functions, as well as other nanoscale structures of biological and abiological origins. Recent advances in computational tools, particularly t...

The centralized power generation infrastructure that defines the North American electric grid is slowly moving to the distributed architecture due to the explosion in use of renewable generation and distributed energy resources (DERs), such as residential solar, wind turbines and battery storage. Furthermore, variable pricing policies and profusion...

A wide variety of real life complex networks are prohibitively large for modeling, analysis and control. Understanding the structure and dynamics of such networks entails creating a smaller representative network that preserves its relevant topological and dynamical properties. While modern machine learning methods have enabled identification of go...

This paper develops a novel Continuous-time Accelerated Proximal Point Algorithm (CAPPA) for $\ell_1$-minimization problems with provable fixed-time convergence guarantees. The problem of $\ell_1$-minimization appears in several contexts, such as sparse recovery (SR) in Compressed Sensing (CS) theory, and sparse linear and logistic regressions in m...

Graph convolutional networks (GCNs) are a widely used method for graph representation learning. We investigate the power of GCNs, as a function of their number of layers, to distinguish between different random graph models on the basis of the embeddings of their sample graphs. In particular, the graph models that we consider arise from graphons, w...

This letter develops a novel Continuous-Time Accelerated Proximal Point Algorithm (CAPPA) for $\ell _1$-minimization problems with provable fixed-time convergence guarantees. The problem of $\ell _1$-minimization appears in several contexts such as Sparse Recovery (SR) in Compressed Sensing (CS) theory and sparse linear and logistic regressions in...

Motivation:
Understanding the mechanisms and structural mappings between molecules and pathway classes is critical for design of reaction predictors for synthesizing new molecules. This paper studies the problem of prediction of classes of metabolic pathways (series of chemical reactions occurring within a cell) in which a given biochemical compou...

The centralized power generation infrastructure that defines the U.S. electric grid is slowly moving to the distributed architecture due to the explosion in use of renewable generation and distributed energy resources (DERs), such as residential solar, wind turbines and battery storage. Furthermore, variable pricing policies and profusion of flexib...

Graph convolutional networks (GCNs) are a widely used method for graph representation learning. We investigate the power of GCNs, as a function of their number of layers, to distinguish between different random graph models on the basis of the embeddings of their sample graphs. In particular, the graph models that we consider arise from graphons, w...

In this paper, the \textit{fixed}-\textit{time} stability of a novel proximal dynamical system is investigated for solving mixed variational inequality problems. Under the assumptions of strong monotonicity and Lipschitz continuity, it is shown that the solution of the proposed proximal dynamical system exists in the classical sense, is uniquely de...

Typically clustering algorithms provide clustering solutions with prespecified number of clusters. The lack of a priori knowledge on the true number of underlying clusters in the dataset makes it important to have a metric to compare the clustering solutions with different number of clusters. This article quantifies a notion of persistence of clust...

This work presents a maximum entropy principle based algorithm for solving minimum multiway $k$-cut problem defined over static and dynamic {\em digraphs}. A multiway $k$-cut problem requires partitioning the set of nodes in a graph into $k$ subsets, such that each subset contains one prespecified node, and the corresponding total cut weight is min...

This paper introduces the fixed-time distributed convex optimization problem for continuous time multi-agent systems under time-invariant topology. A novel nonlinear protocol coupled with tools from Lyapunov theory is proposed to minimize the sum of convex objective functions of each agent in fixed-time. Each agent in the network can access only it...

One of the main challenges in cluster analysis is estimating the true number of clusters in a dataset. This paper quantifies a notion of persistence of a clustering solution over a range of resolution scales, which is used to characterize the natural clusters and estimate the true number of clusters in a dataset. We show that this quantification of...

This thesis is divided into two parts. In the first part, I describe efficient meta-heuristic algorithms for a series of combinatorially complex optimization problems, while the second part is concerned with robust and scalable control architecture for a network of paralleled converter/inverter systems (DC/AC microgrids).

This paper addresses the problem of output voltage regulation for multiple DC/DC converters connected to a microgrid, and prescribes a scheme for sharing power among different sources. This architecture is structured in such a way that it admits quantifiable analysis of the closed-loop performance of the network of converters; the analysis simplifi...

This paper discusses a deterministic clustering approach to capacitated resource allocation problems. In particular, the Deterministic Annealing (DA) algorithm from the data-compression literature, which bears a distinct analogy to the phase transformation under annealing process in statistical physics, is adapted to address problems pertaining to...

Atomic force microscopy typically relies on high-resolution high-bandwidth cantilever deflection measurements based control for imaging and estimating sample topography and properties. More precisely, in amplitude-modulation atomic force microscopy (AM-AFM), the control effort that regulates deflection amplitude is used as an estimate of sample top...

This paper discusses a deterministic clustering approach to capacitated resource allocation problems. In particular, the Deterministic Annealing (DA) algorithm from the data-compression literature, which bears a distinct analogy to the phase transformation under annealing process in statistical physics, is adapted to address problems pertaining to...

One of the most important challenges facing an electric grid is to incorporate renewables and distributed energy resources (DERs) to the grid. Because of the associated uncertainties in power generations and peak power demands, opportunities for improving the functioning and reliability of the grid lie in the design of an efficient, yet pragmatic d...

This paper addresses the problem of output voltage regulation for multiple DC-DC converters connected to a grid, and prescribes a robust scheme for sharing power among different sources. Also it develops a method for sharing 120 Hz ripple among DC power sources in a prescribed proportion, which accommodates the different capabilities of DC power so...

This paper aims at control design and its implementation for robust high-bandwidth precision (nanoscale) positioning systems. Even though modern model-based control theoretic designs for robust broadband high-resolution positioning have enabled orders of magnitude improvement in performance over existing model independent designs, their scope is se...

For a long time, signal processing used to be accomplished by microprocessors and DSPs (Digital Signal Processors). The advent of reconfigurable computing devices, such as Complex Programmable Logic Devices (CPLDs) and Field Programmable Gate Arrays (FPGAs) has given a new dimension to signal processing applications by not only allowing users to cu...

This paper derives the equations of motion of variable mass systems using a coordinate-free approach. These equations have been verified with simple models, and the terms originating in the steady and unsteady gas-dynamic interaction effects have been used in the modeling and simulation of the propulsive phase of the Supersonic Inflatable Advanced...

This paper presents a method for detecting abnormal motion in real time using a computer vision system. The method is based on the modeling of human body image, which takes into account both orientation and velocity of prominent body parts. A comparative study is made of this method with other existing algorithms based on optical flow and the use o...

## Questions

Questions (4)

Hi, I'm looking for an affordable ($5k) grid simulator hardware with 1kVA output/input capacity. Any suggestions? Thanks

Hi,

I was wondering if there is any PWM controlled power electronics converter topology that facilitates converting an input AC signal (with voltage amplitude V1 and frequency w1) to another AC signal (with amplitude V2 and frequency w2). Essentially in developing countries, the grid output is often variable, i.e., instead of rated 220V 50Hz, the output of the grid often varies from 180V-250V 40-60Hz. Such variable voltage can potentially damage any device rated to operate at 220V 50Hz. I'm relatively new to power electronics and only aware of basic DC-DC and DC-AC converter topologies. Thanks.

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