Amit Surana

• Doctor of Philosophy
• Group Leader at United Technologies Research Center

We present a novel variational quantum framework for nonlinear partial differential equation (PDE) constrained optimization problems. The proposed work extends the recently introduced bi-level variational quantum PDE constrained optimization (BVQPCO) framework for linear PDE to a nonlinear setting by leveraging Carleman linearization (CL). CL frame...

We present a novel variational quantum framework for partial differential equation (PDE) constrained design optimization problems. Such problems arise in simulation based design in many scientific and engineering domains. For instance in aerodynamic design, the PDE constraints are the conservation laws such as momentum, mass and energy balance, the...

Biomarkers enable objective monitoring of a given cell or state in a biological system and are widely used in research, biomanufacturing, and clinical practice. However, identifying appropriate biomarkers that are both robustly measurable and capture a state accurately remains challenging. We present a framework for biomarker identification based u...

Biomarker selection and real-time monitoring of cell dynamics remains an active challenge in cell biology and biomanufacturing. Here, we develop scalable adaptations of classic approaches to sensor selection for biomarker identification on several transcriptomics and biological datasets that are otherwise cannot be studied from a controls perspecti...

In this paper, we present an efficient quantum algorithm to simulate nonlinear differential equations with polynomial vector fields of arbitrary (finite) degree on quantum platforms. Ordinary differential equations (ODEs) and partial differential equations (PDEs) arise extensively in science and engineering applications. Examples of ODE models incl...

Recent advances in biological technologies, such as multi-way chromosome conformation capture (3C), require development of methods for analysis of multi-way interactions. Hypergraphs are mathematically tractable objects that can be utilized to precisely represent and analyze multi-way interactions. Here we present the Hypergraph Analysis Toolbox (H...

This article offers a system-theoretic analysis of networks of networks that are formed via the Kronecker product of hypergraphs. Hypergraphs generalize graph theory to account for multiway relationships which are ubiquitous in many real-world systems. We extend the notion of the matrix Kronecker product to a tensor Kronecker product, present vario...

In this paper we develop a framework to study observability for uniform hypergraphs. Hypergraphs are generalizations of graphs in which edges may connect any number of nodes, thereby representing multi-way relationships which are ubiquitous in many real-world networks including neuroscience, social networks, and bioinformatics. We define a canonica...

Multi-laser powder bed fusion (M-LPBF) systems are garnering increased attention in metal additive manufacturing as they promise increased productivity and part size without sacrificing feature resolution or mechanical properties. However, M-LPBF introduce unique problems related to the interaction of multiple moving heat sources not observed in si...

We present an efficient quantum algorithm to simulate nonlinear differential equations with polynomial vector fields of arbitrary degree on quantum platforms. Models of physical systems that are governed by ordinary differential equations (ODEs) or partial differential equation (PDEs) can be challenging to solve on classical computers due to high d...

Recent multi-way chromosome conformation capture (3C) technologies, such as Pore-C, require development of methods for analysis of multi-way interactions. Hypergraphs are mathematically tractable objects that can be utilized to precisely represent and analyze multi-way interactions. Here we present the hypergraph analysis toolbox (HAT), a software...

Chromatin architecture, a key regulator of gene expression, can be inferred using chromatin contact data from chromosome conformation capture, or Hi-C. However, classical Hi-C does not preserve multi-way contacts. Here we use long sequencing reads to map genome-wide multi-way contacts and investigate higher order chromatin organization in the human...

In this paper we present a novel framework for hypergraph similarity measures (HSMs) for hypergraph comparison. Hypergraphs are generalizations of graphs in which edges may connect any number of vertices, thereby representing multi-way relationships which are ubiquitous in many real world networks including neuroscience, social networks, and bioinf...

Chromatin architecture, a key regulator of gene expression, is inferred through chromatin contacts. However, classical analyses of chromosome conformation data do not preserve multi-way relationships. Here we use long sequencing reads to map genome-wide multi-way contacts and investigate higher order chromatin organization of the human genome. We u...

In this paper, we propose two novel approaches for hypergraph comparison. The first approach transforms the hypergraph into a graph representation for use of standard graph dissimilarity measures. The second approach exploits the mathematics of tensors to intrinsically capture multi-way relations. For each approach, we present measures that assess...

In this paper, we develop a notion of controllability for hypergraphs via tensor algebra and polynomial control theory. Inspired by uniform hypergraphs, we propose a new tensor-based multilinear dynamical system representation, and derive a Kalman-rank-like condition to determine the minimum number of control nodes (MCN) needed to achieve controlla...

We propose an interdisciplinary framework for time series classification, forecasting, and anomaly detection by combining concepts from Koopman operator theory, machine learning, and linear systems and control theory. At the core of this framework is nonlinear dynamic generative modeling of time series using the Koopman operator which is an infinit...

In this paper we apply guided policy search (GPS) based reinforcement learning framework for a high dimensional optimal control problem arising in an additive manufacturing process. The problem comprises of controlling the process parameters so that layer-wise deposition of material leads to desired geometric characteristics of the resulting part s...

In this paper we explore the application of simultaneous move Monte Carlo Tree Search (MCTS) based online framework for tactical maneuvering between two unmanned aircrafts. Compared to other techniques, MCTS enables efficient search over long horizons and uses self-play to select best maneuver in the current state while accounting for the opponent...

In this chapter, we overview a new approach for nonlinear estimation based on Koopman operator-theoretic framework. We exploit Koopman eigenfunctions to create a nonlinear embedding/lifting of underlying nonlinear dynamics to synthesize observer forms (which we call Koopman observer form (KOF)) which enables the use of well-known estimation techniq...

In this paper, we explore the role of tensor algebra in the balanced truncation (BT) based model reduction/identification for high-dimensional multilinear/linear time invariant systems. Tensor decompositions facilitate discovery of hidden patterns/redundancies in multiway data by capturing higher-order interactions and couplings to obtain highly co...

A framework is introduced for planning unmanned aerial vehicle flight paths for visual surveillance of ground targets, each having particular viewing requirements. Specifically, each target is associated with a set of imaging parameters, including a desired (i) tilt angle, (ii) azimuth, with the option of a 360-degree view, and (iii) dwell-time. To...

In this paper, we provide a system theoretic treatment of a new class of multilinear time invariant (MLTI) systems in which the states, inputs and outputs are tensors, and the system evolution is governed by multilinear operators. The MLTI system representation is based on the Einstein product and even-order paired tensors. There is a particular te...

In biological and engineering systems, structure, function and dynamics are highly coupled. Such interactions can be naturally and compactly captured via tensor based state space dynamic representations. However, such representations are not amenable to the standard system and controls framework which requires the state to be in the form of a vecto...

With the advent of modern expert systems driven by deep learning that supplement human experts (e.g. radiologists, dermatologists, surveillance scanners), we analyze how and when do such expert systems enhance human performance in a fine-grained small target visual search task. We set up a 2 session factorial experimental design in which humans vis...

A framework is introduced for planning unmanned aerial vehicle (UAV) flight paths for visual surveillance of ground targets, each having particular viewing requirements. Specifically, the framework is designed for instances in which each target is associated with a set of imaging parameters, including a desired: (i) tilt angle, (ii) azimuth, with t...

A supervisory mission in which a team of unmanned vehicles visits a set of targets and collects sensory data to be analyzed in real time by a remotely located human operator is considered. A framework is proposed to simultaneously construct the operator’s task-processing schedule and each vehicle’s target visitation route, with the dual goal of mod...

This paper reviews and extends the recent work on signed real measure of regular languages within a unified framework. The language measure provides total ordering of partially ordered sets of sublanguages of a regular language to allow quantitative evaluation of the controlled behavior of deterministic finite state automata under different supervi...

Manned-Unmanned Teaming (MUM-T) is a military concept that employs unmanned aerial systems (UASs) in support of traditional manned aircraft. The current ratio of manned to unmanned aircraft in MUM-T operations is one to one with a goal to expand to multiple UASs to further enhance the capability, but this imposes significant challenges on the opera...

A cloud-supported coverage control scheme is proposed for multi-agent, persistent surveillance missions. This approach decouples assignment from motion planning operations in a modular framework. Coverage assignments and surveillance parameters are managed on the cloud and transmitted to mobile agents via unplanned and asynchronous exchanges. These...

This paper outlines the development and testing of a novel, feedback-enabled attention allocation aid (AAAD), which uses real-time physiological data to improve human performance in a realistic sequential visual search task. Indeed, by optimizing over search duration, the aid improves efficiency, while preserving decision accuracy, as the operator...

This paper outlines the development and testing of a novel, feedback-enabled attention allocation aid (AAAD), which uses real-time physiological data to improve human performance in a realistic sequential visual search task. Indeed, by optimizing over search duration, the aid improves efficiency, while preserving decision accuracy, as the operator...

In this paper we develop a new approach for observer synthesis for discrete time autonomous nonlinear systems based on Koopman operator theoretic framework. Koopman operator is a linear but an infinite-dimensional operator that governs the time evolution of system outputs in a linear fashion. We exploit this property to synthesize an observer form...

In this paper we propose an extension of Koopman operator framework for non-autonomous systems with periodic and quasi-periodic time dependence. Using a time parametrized family of Koopman operators and the associated time dependent eigenvalues and eigenfunctions, and concepts from Floquet theory, we extend the notion of the Koopman Mode Decomposit...

A decomposition-based coverage control scheme is proposed for multi-agent, persistent surveillance missions operating in a communication-constrained, dynamic environment. The proposed approach decouples high-level task assignment from low-level motion planning in a modular framework. Coverage assignments and surveillance parameters are managed by a...

This article focuses on the design of systems in which a human operator is responsible for overseeing autonomous agents and providing feedback based on sensor data. In the control systems community, the term human supervisory control (or simply supervisory control) is often used as a shorthand reference for systems with this type of architecture [5...

Dynamic texture (DT) is a simple yet powerful paradigm to model videos with repetitive spatiotemporal behavior. In this paper we propose a novel nonlinear approach for modeling complex DTs based on Koopman operator theoretic method. Koopman operator is linear but infinite dimensional operator, and captures full nonlinear behavior. We exploit this a...

Simultaneous localization and mapping (SLAM) algorithms allow a single robot to reduce the effects of drifting sensor biases while exploring unknown, GPS-denied environments. To reduce exploration time, a team of robots can build smaller maps in parallel and perform map fusion. Most map fusion techniques require a known relative transformation betw...

Task-based, rather than vehicle-based, control architectures have been shown to provide superior performance in certain human supervisory control missions. These results motivate the need for the development of robust, reliable usability metrics to aid in creating interfaces for use in this domain. To this end, we conduct a pilot usability study of...

A closed loop approach for surveillance was developed leveraging the Spectral Multiscale Coverage (SMC) algorithm for sensor management coupled with the Cardinalized Probability Hypothesis Density (CPHD) multitarget tracker. Additionally, the CPHD was formulated such that it is able to ingest features, if available. Simulations with fixed and mobil...

Can a dynamical system paint masterpieces such as Da Vinci's Mona Lisa or Monet's Water Lilies? Moreover, can this dynamical system be chaotic in the sense that although the trajectories are sensitive to initial conditions, the same painting is created every time? Setting aside the creative aspect of painting a picture, in this work, we develop a n...

We use dynamic active contours driven by optimal mass transport optical flow to detect crowd behaviors, in particular crowd merging, splitting and collision events. The overall framework is variational, and thus one could very naturally formulate functionals which include geometric active contours together with optical flow ideas. This allows to fu...

In this paper we propose an approach for unsupervised Inverse Reinforcement Learning (IRL) with noisy data using a hidden variable Markov Decision Processes (hMDP) representation. hMDP accounts for observation uncertainty by using a hidden state variable. We develop a nonparametric Bayesian IRL technique for hMDP based on Dirichlet Processes mixtur...

In this paper we develop a Bayesian nonparametric Inverse Reinforcement Learning technique for switched Markov Decision Processes (MDP). Similar to switched linear dynamical systems, switched MDP (sMDP) can be used to represent complex behaviors composed of temporal transitions between simpler behaviors each represented by a standard MDP. We use st...

We propose a hierarchical planning framework for mission planning and execution in uncertain and dynamic environments. We consider missions that involve motion planning in large, cluttered environments, trading off mission objectives while satisfying logical/spatial/temporal constraints. Our framework enables the decomposition of the planning probl...

In this work, two multitarget trackers - the Cardinalized Probability Hypothesis Density (CPHD) filter and the Recursive Random Sample Consensus (R-RANSAC) algorithm - were applied to three scenarios of the Video Verification of IDentity (VIVID) dataset provided by DARPA. The dataset consists of real video data of multiple cars observed from an unm...

There has recently been a significant amount of activity in developing supervisory control algorithms for multiple unmanned aerial vehicle operation by a single operator. While previous work has demonstrated the favorable impacts that arise in the introduction of increasingly sophisticated autonomy algorithms, little work has performed an explicit...

We demonstrate a dynamical system framework based on motion patterns for detecting anomalous individual and group behavior in complex videos. We first describe a framework based on trajectory modeling, in which coarse statistical models are used to capture global motion patterns, and are employed in change detection to identify anomalous behavior a...

We study the mixed human-robot team design in a system theoretic setting using the context of a surveillance mission. The three key coupled components of a mixed team design are (i) policies for the human operator, (ii) policies to account for erroneous human decisions, and (iii) policies to control the automaton. In this paper, we survey elements...

We discuss the implementation of Spectral Multiscale Coverage (SMC) based multi-vehicle control and coordination for coverage and search missions by autonomous UAVs. The SMC algorithm gives rise to multiscale vehicle trajectories leading to efficient coverage of a given area and thereby making it useful for search algorithms that are robust to sens...

In this paper, we propose a hierarchical mission planner where the state of the world and of the mission are abstracted into corresponding states of a Markov Decision Process (MDP). Transitions in the MDP represent abstract motion actions that are planned by a lower level probabilistic planner. The cost structure of the MDP is multi-dimensional: ea...

We present an efficient planning algorithm for allocation and scheduling of spatially distributed tasks to multiple heterogenous resources (e.g. mobile sensors, robots) in presence of ordering constraints on task execution and environmental uncertainties. We use Process Algebra (PA) for capturing such constraints. Building on probabilistic timed PA...

We propose multiscale metrics to capture the quality of coverage by a static configuration of agents. This metric is used for the locational optimization of sensor networks. Agent configurations that minimize the multiscale coverage metric are an alternative to the well-known centroidal voronoi tesselations. Other applications include quantization...

While there is currently significant interest in developing Unmanned Aerial Systems (UASs) that can be supervised by a single operator, the majority of these systems focus on Intelligence, Surveillance, and Reconnaissance (ISR) domains. One domain that has received significantly less attention is the use of multiple UASs to insert or extract suppli...

We propose an optimization framework to study two fundamental attention control aspects in human-robot systems: Where and how much attention should the operator allocate? In other words, which information source should be observed by the operator, and how much time duration should be allocated to the information feed in order to optimize the overal...

We present a multiscale adaptive search algorithm for efficiently searching an unknown number of stationary targets using a team of multiple mobile sensors. We first derive a Spectral Multiscale Coverage (SMC) control law for a Dubins vehicle model. Given a search prior, the SMC control leads to uniform coverage dynamics for the mobile sensors such...

Retrofitting the existing building stock represents the largest and fastest way to reduce energy consumption for the DoD. However the current retrofit delivery process is manually intensive and expensive, focused on equipment selection for initial cost and not energy performance, and the design tools are not amenable to systems solutions that have...

The paper describes the application of a methodology and tools for early assessment of integrated system solutions for deep retrofit with a potential of 30-50% energy reductions across large building portfolio as well as at an individual building scale using information only about building characteristics, usage type and climate. The building energ...

In this paper we address the problem of uncertainty management for robust design, and verification of large dynamic networks whose performance is affected by an equally large number of uncertain parameters. Many such networks (e.g. power, thermal and communication networks) are often composed of weakly interacting subnetworks. We propose intrusive...

Building heating and cooling systems have potential for energy savings by employing passive devices that exploit thermal stratification and buoyancy. The resulting thermal-fluid flow patterns from such systems tend to be sensitive to disturbances, and advanced flow control techniques are important to maintain occupant comfort. In this work, we empl...

For the universal hypothesis testing problem, where the goal is to decide between the known null hypothesis distribution and some other unknown distribution, Hoeffding proposed a universal test in the nineteen sixties. Hoeffding's universal test statistic can be written in terms of Kullback-Leibler (K-L) divergence between the empirical distributio...

Development of robust dynamical systems and networks such as autonomous aircraft systems capable of accomplishing complex missions faces challenges due to the dynamically evolving uncertainties coming from model uncertainties, necessity to operate in a hostile cluttered urban environment, and the distributed and dynamic nature of the communication...

In this paper we address the problem of uncertainty management for robust design, and verification of large dynamic networks whose performance is affected by an equally large number of uncertain parameters. Many such networks (e.g. power, thermal and communication networks) are often composed of weakly interacting subnetworks. We propose an iterati...

Neuro-dynamic programming encompasses techniques from both reinforcement learning and approximate dynamic programming. Feature selection refers to the choice of basis that defines the function class that is required in the application of these techniques. This chapter reviews two popular approaches to neuro-dynamic programming, TD- and Q-Learning....

In surveillance problems, the uncertainty in the position of a target can be specified in terms of a probability distribution. To reduce the average search times to detect a target using mobile sensors, it is desirable to have the trajectories of the sensors sample the probability distribution uniformly. When the target is moving, the initial uncer...

In this paper we develop a copula based approximation framework for scalable analysis of Stochastic Automata Networks (SAN) arising in reliability analysis, and can be described by CTMCs. Copulas provide a general approach to model joint distributions in terms of their marginals. Using copulas functions, the dependencies between the interacting aut...

The application of distributed parameter control to spatiotemporal thermo-fluid systems requires the use of model reduction methods. The form of the optimal feedback control can inform design decisions, such as sensor and actuator selection and placement. A number of model reduction approaches for fluid systems have been put forward that are based...

The Lawrence Berkeley National Laboratory (LBNL), the University of California Merced (UCM), and the United Technologies Research Center (UTRC) conducted field studies and modeling analyses in the Classroom and Office Building (COB) and the Science and Engineering Building (S&E) at the University of California, Merced. In the first year, of a plann...

We introduce the sensor-utility-network (SUN) method for occupancy estimation in buildings. Based on inputs from a variety of sensor measurements, along with historical data regarding building utilization, the SUN estimator produces occupancy estimates through the solution of a receding-horizon convex optimization problem. State-of-the-art on-line...

Current climate control systems often rely on building regulation maximum occupancy numbers for maintaining proper temperatures. However, in many situations, there are rooms that are used infrequently, and may be heated or cooled needlessly. Having knowledge regarding occupancy and being able to accurately predict usage patterns may al-low signific...

For the universal hypothesis testing problem, where the goal is to decide between the known null hypothesis distribution and some other unknown distribution, Hoeffding proposed a universal test in the nineteen sixties. Hoeffding's universal test statistic can be written in terms of Kullback-Leibler (K-L) divergence between the empirical distributio...

Commercial buildings are responsible for a significant fraction of the energy consumption and greenhouse gas emissions in the U.S. and worldwide. Consequently, the design, optimization and control of energy efficient buildings can have a tremendous impact on energy cost and greenhouse gas emission. Buildings are complex, multi-scale in time and spa...

The support vector machine (SVM) has emerged as one of the most popular approaches to classification and supervised learning. It is a flexible approach for solving the problems posed in these areas, but the approach is not easily adapted to noisy data in which absolute discrimination is not possible. We address this issue in this paper by returning...

We develop a nonlinear theory for separation and attachment on no-slip boundaries of three-dimensional unsteady flows that have a steady mean component. In such flows, separation and attachment surfaces turn out to originate from fixed lines on the boundary, even though the surfaces themselves deform in time. The exact separation geometry is not ca...

In this paper we propose a Lagrangian coherent structures (LCS) based approach to modeling and estimation of contaminant transport and mixing in large indoor spaces in buildings. Specifically, we show how the knowledge of LCS can be exploited to enhance proper orthogonal decomposition (POD) based model reduction, sensor placement and comparing effe...

Consider a structurally stable nonhyperbolic critical manifold in a nonautonomous dynamical system. Despite the lack of hyperbolicity and slow dynamics, sharp moving spikes can develop and move along this critical manifold. An important physical example of this phenomenon is moving separation behind a cylinder in accelerating crossflow. Using a com...

We consider the motion of both point vortices and uniform vortex patches in arbitrary, possibly multiply connected, regions bounded by impenetrable walls on the surface of a sphere. By exploiting knowledge of the functional form of the relevant Green’s function in a pre-image circular domain that is conformally equivalent to a stereographic project...