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Quantum electrodynamics in 1 + 1D (QED2) shares intriguing properties with QCD, including confinement, string breaking, and interesting phase diagram when the non-trivial topological $\theta$-term is considered. Its lattice regularization is a commonly used toy model for quantum simulations of gauge theories on near-term quantum devices. In this work, we address algorithms for adiabatic state preparation in digital quantum simulations of QED2. We demonstrate that, for specific choices of parameters, the existing adiabatic procedure leads to level crossing between states of different charge sectors, preventing the correct preparation of the ground state. We further propose a new adiabatic Hamiltonian and verify its efficiency in targeting systems with a nonzero topological $\theta$-term and in studying string breaking phenomena., Comment: 12 pages, 5 figures

We investigate how growth of an oxide film will influence the deuterium recycling properties of Li on plasma facing surfaces. Lithium films on the walls or plasma-facing material surfaces of a fusion vacuum vessel improves plasma performance in part by removing residual impurity atoms from the plasma. Oxygen atoms, from residual water vapor or eroded oxide surfaces, are a prominent ubiquitous impurity that can create a layer of oxide on lithium films. The results of the work herein also indicate a possibility for determining the thickness of the oxide layer on such Li films by measuring the reflection probability of incident deuterium particles from the surface., Comment: 5 pages, 4 figures

Stability theory plays a crucial role in feedback control. However, adaptive control theory requires advanced and specialized stability notions that are not frequently used in standard feedback control theory. The present document is a set of notes for a graduate course. It describes the global stability notions needed in (robust) adaptive control and develops the mathematical tools that are used for the proof of such stability properties. Moreover, the document shows why and how these global stability properties arise in adaptive control. We focus on stability properties for time-invariant systems. Consequently, tracking control problems are not covered by the present document., Comment: 48 pages, 4 figures

Systems modeled by partial differential equations (PDEs) are at least as ubiquitous as systems that are by nature finite-dimensional and modeled by ordinary differential equations (ODEs). And yet, systematic and readily usable methodologies, for such a significant portion of real systems, have been historically scarce. Around the year 2000, the backstepping approach to PDE control began to offer not only a less abstract alternative to PDE control techniques replicating optimal and spectrum assignment techniques of the 1960s, but also enabled the methodologies of adaptive and nonlinear control, matured in the 1980s and 1990s, to be extended from ODEs to PDEs, allowing feedback synthesis for physical and engineering systems that are uncertain, nonlinear, and infinite-dimensional. The PDE backstepping literature has grown in its nearly a quarter century of development to many hundreds of papers and nearly a dozen books. This survey aims to facilitate the entry, for a new researcher, into this thriving area of overwhelming size and topical diversity. Designs of controllers and observers, for parabolic, hyperbolic, and other classes of PDEs, in one and more dimensions (in box and spherical geometries), with nonlinear, adaptive, sampled-data, and event-triggered extensions, are covered in the survey. The lifeblood of control are technology and physics. The survey places a particular emphasis on applications that have motivated the development of the theory and which have benefited from the theory and designs: applications involving flows, flexible structures, materials, thermal and chemically reacting dynamics, energy (from oil drilling to batteries and magnetic confinement fusions), and vehicles., Comment: Preprint submitted to Automatica

Populations do not only interact over time but also age over time. It is therefore common to model them as age-structured PDEs, where age is the space variable. Since the models also involve integrals over age, both in the birth process and in the interaction among species, they are in fact integro-partial differential equations (IPDEs) with positive states. To regulate the population densities to desired profiles, harvesting is used as input. But non-discriminating harvesting, where wanting to repress one species will inevitably repress the other species as well, the positivity restriction on the input (no insertion of population), and the multiplicative nature of harvesting, makes control challenging even for ODE versions of such dynamics, let alone for their IPDE versions on an infinite-dimensional nonnegative state space. We introduce a design for a benchmark version of such a problem: a two-population predator-prey setup. The model is equivalent to two coupled ordinary differential equations (ODEs), actuated by harvesting which must not drop below zero, and strongly disturbed by two autonomous but exponentially stable integral delay equations (IDEs). We develop two control designs. With a modified Volterra-like control Lyapunov function, we design a simple feedback which employs possibly negative harvesting for global stabilization of the ODE model, while guaranteeing regional regulation with positive harvesting. With a more sophisticated, restrained controller we achieve regulation for the ODE model globally, with positive harvesting. For the full IPDE model, with the IDE dynamics acting as large disturbances, for both the simple and saturated feedback laws we provide explicit estimates of the regions of attraction. The paper charts a new pathway for control designs for infinite-dimensional multi-species dynamics and for nonlinear positive systems with positive controls., Comment: submitted to IEEE Transactions on Automatic Control

We report on our ongoing lattice QCD computation of antistatic-antistatic potentials in the presence of two light quarks using the CLS $N_f=2$ gauge configurations and the OpenQ*D codebase. We utilize a set of 16 creation operators, corresponding to 8 sectors characterized by angular momentum and parity quantum numbers for light quarks $qq = (ud - du) / \sqrt{2}$ (isospin $0$), $qq \in \{ uu , (ud + du) / \sqrt{2}, dd \}$ (isospin $1$) and $qq \in \{ us , ds \}$ (isospin $1/2$ and strangeness $-1$). We improve on previous work by considering a large number of off-axis separations of the static antiquarks and by using tree-level improvement. The resulting potentials provide vague indication for one-pion exchange at $\bar Q \bar Q$ separations $r \gtrapprox$ 0.5 fm., Comment: 9 pages, 4 figures, contribution as a parallel talk to the 41st Lattice Conference (Lattice 2024), July 28 - August 3, 2024. Liverpool, United Kingdom

Fixed-time stable dynamical systems are capable of achieving exact convergence to an equilibrium point within a fixed time that is independent of the initial conditions of the system. This property makes them highly appealing for designing control, estimation, and optimization algorithms in applications with stringent performance requirements. However, the set of tools available for analyzing the interconnection of fixed-time stable systems is rather limited compared to their asymptotic counterparts. In this paper, we address some of these limitations by exploiting the emergence of multiple time scales in nonlinear singularly perturbed dynamical systems, where the fast dynamics and the slow dynamics are fixed-time stable on their own. By extending the so-called composite Lyapunov method from asymptotic stability to the context of fixed-time stability, we provide a novel class of Lyapunov-based sufficient conditions to certify fixed-time stability in a class of singularly perturbed dynamical systems. The results are illustrated, analytically and numerically, using a fixed-time gradient flow system interconnected with a fixed-time plant and an additional high-order example.

We employ the recent performance-barrier event-triggered control (P-ETC) for achieving global exponential convergence of a class of reaction-diffusion PDEs via PDE backstepping control. Rather than insisting on a strictly monotonic decrease of the Lyapunov function for the closed-loop system, P-ETC allows the Lyapunov function to increase as long as it remains below an acceptable performance-barrier. This approach integrates a performance residual, the difference between the value of the performance-barrier and the Lyapunov function, into the triggering mechanism. The integration adds flexibility and results in fewer control updates than with regular ETC (R-ETC) that demands a monotonic decrease of the Lyapunov function. Our P-ETC PDE backstepping design ensures global exponential convergence of the closed-loop system in the spatial L^2 norm, without encountering Zeno phenomenon. To avoid continuous monitoring of the triggering function that generates events, we develop periodic event-triggered and self-triggered variants (P-PETC and P-STC, respectively) of the P-ETC. The P-PETC only requires periodic evaluation of the triggering function whereas the P-STC preemptively computes the time of the next event at the current event time using the system model and continuously available system states. The P-PETC and P-STC also ensure a Zeno-free behavior and deliver performance equivalent to that of the continuous-time P-ETC which requires continuous evaluation of the triggering function, in addition to the continuous sensing of the state. We provide numerical simulations to illustrate the proposed technique and to compare it with R-ETC associated with strictly decreasing Lyapunov functions.

Physical side-channel attacks represent a great challenge for today's chip design. Although attacks on CMOS dynamic power represent a class of state-of-the-art attacks, many other effects potentially affect the security of CMOS chips analogously by affecting mostly static behaviour of the chip, including aging, ionizing radiation, or non-ionizing illumination of the CMOS. Vulnerabilities exploiting data dependency in CMOS static power were already demonstrated in practice and the analogous vulnerability exploiting light-modulated static power was demonstrated by simulation. This work confirms the CMOS vulnerability related to the light-modulated data-dependent static power experimentally and discusses future work., Comment: 8 figures

Security requirements for the Internet of things (IoT), wireless sensor nodes, and other wireless devices connected in a network for data exchange are high. These devices are often subject to lab analysis with the objective to reveal secret hidden information. One kind of attacks to reveal the cryptographic key is to perform optical Fault Injection attacks. In this work, we investigated the IHP radiation tolerant shift registers built of Triple Modular Redundant flip-flops. In our experiments, we were able to inject different transient faults into TMR registers., Comment: 2 figures, 1 table

In socio-technical multi-agent systems, deception exploits privileged information to induce false beliefs in "victims," keeping them oblivious and leading to outcomes detrimental to them or advantageous to the deceiver. We consider model-free Nash-equilibrium-seeking for non-cooperative games with asymmetric information and introduce model-free deceptive algorithms with stability guarantees. In the simplest algorithm, the deceiver includes in his action policy the victim's exploration signal, with an amplitude tuned by an integrator of the regulation error between the deceiver's actual and desired payoff. The integral feedback drives the deceiver's payoff to the payoff's reference value, while the victim is led to adopt a suboptimal action, at which the pseudogradient of the deceiver's payoff is zero. The deceiver's and victim's actions turn out to constitute a "deceptive" Nash equilibrium of a different game, whose structure is managed - in real time - by the deceiver. We examine quadratic, aggregative, and more general games and provide conditions for a successful deception, mutual and benevolent deception, and immunity to deception. Stability results are established using techniques based on averaging and singular perturbations. Among the examples in the paper is a microeconomic duopoly in which the deceiver induces in the victim a belief that the buyers disfavor the deceiver more than they actually do, leading the victim to increase the price above the Nash price, and resulting in an increased profit for the deceiver and a decreased profit for the victim. A study of the deceiver's integral feedback for the desired profit reveals that, in duopolies with equal marginal costs, a deceiver that is greedy for very high profit can attain any such profit, and pursue this with arbitrarily high integral gain (impatiently), irrespective of the market preference for the victim.

Neural operator approximations of the gain kernels in PDE backstepping has emerged as a viable method for implementing controllers in real time. With such an approach, one approximates the gain kernel, which maps the plant coefficient into the solution of a PDE, with a neural operator. It is in adaptive control that the benefit of the neural operator is realized, as the kernel PDE solution needs to be computed online, for every updated estimate of the plant coefficient. We extend the neural operator methodology from adaptive control of a hyperbolic PDE to adaptive control of a benchmark parabolic PDE (a reaction-diffusion equation with a spatially-varying and unknown reaction coefficient). We prove global stability and asymptotic regulation of the plant state for a Lyapunov design of parameter adaptation. The key technical challenge of the result is handling the 2D nature of the gain kernels and proving that the target system with two distinct sources of perturbation terms, due to the parameter estimation error and due to the neural approximation error, is Lyapunov stable. To verify our theoretical result, we present simulations achieving calculation speedups up to 45x relative to the traditional finite difference solvers for every timestep in the simulation trajectory., Comment: 13 pages, 4 figures

Entangled two-photon absorption can enable a linear scaling of fluorescence emission with the excitation power. In comparison to classical two-photon absorption with a quadratic scaling, this can allow fluorescence imaging or photolithography with high axial resolution at minimal exposure intensities. However, most experimental studies on two-photon absorption were not able to show an unambiguous proof of fluorescence emission driven by entangled photon pairs. On the other hand, existing theoretical models struggle to accurately predict the entangled-two-photon-absorption behavior of chemically complex dyes. In this paper, we introduce an approach to simulate entangled two-photon absorption in common fluorescence dyes considering their chemical properties. Our theoretical model allows a deeper understanding of experimental results and thus the occurrence of entangled two-photon absorption. In particular, we found a remarkable dependency of the absorption probability on the phase-matching temperature of the nonlinear material. Further, we compared results of our theoretical approach to experimental data for Nile Red., Comment: 23 pages, 5 figures

This paper deals with the gradient extremum seeking control for static scalar maps with actuators governed by distributed diffusion partial differential equations (PDEs). To achieve the real-time optimization objective, we design a compensation controller for the distributed diffusion PDE via backstepping transformation in infinite dimensions. A further contribution of this paper is the appropriate motion planning design of the so-called probing (or perturbation) signal, which is more involved than in the non-distributed counterpart. Hence, with these two design ingredients, we provide an averaging-based methodology that can be implemented using the gradient and Hessian estimates. Local exponential stability for the closed-loop equilibrium of the average error dynamics is guaranteed through a Lyapunov-based analysis. By employing the averaging theory for infinite-dimensional systems, we prove that the trajectory converges to a small neighborhood surrounding the optimal point. The effectiveness of the proposed extremum seeking controller for distributed diffusion PDEs in cascade of nonlinear maps to be optimized is illustrated by means of numerical simulations., Comment: 8 pages and 7 figures

This paper presents a complete inverse optimality study for a linearized tank-liquid system where the liquid is described by the viscous Saint-Venant model with surface tension and possible wall friction. We define an appropriate weak solution notion for which we establish existence/uniqueness results with inputs that do not necessarily satisfy any compatibility condition as well as stabilization results with feedback laws that are constructed with the help of a Control Lyapunov Functional. We show that the proposed family of stabilizing feedback laws is optimal for a certain meaningful quadratic cost functional. Finally, we show that the optimal feedback law guarantees additional stronger stability estimates which are similar to those obtained in the case of classical solutions., Comment: 40 pages

This paper introduces a new method to achieve stable convergence to Nash equilibrium in duopoly noncooperative games. Inspired by the recent fixed-time Nash Equilibrium seeking (NES) as well as prescribed-time extremum seeking (ES) and source seeking schemes, our approach employs a distributed sliding mode control (SMC) scheme, integrating extremum seeking with sinusoidal perturbation signals to estimate the pseudogradients of quadratic payoff functions. Notably, this is the first attempt to address noncooperative games without relying on models, combining classical extremum seeking with relay components instead of proportional control laws. We prove finite-time convergence of the closed-loop average system to Nash equilibrium using stability analysis techniques such as time-scaling, Lyapunov's direct method, and averaging theory for discontinuous systems. Additionally, we quantify the size of residual sets around the Nash equilibrium and validate our theoretical results through simulations., Comment: 8 pages and 2 figures. arXiv admin note: substantial text overlap with arXiv:2404.07287

For a map that is strictly but not strongly convex, model-based gradient extremum seeking has an eigenvalue of zero at the extremum, i.e., it fails at exponential convergence. Interestingly, perturbation-based model-free extremum seeking has a negative Jacobian, in the average, meaning that its (practical) convergence is exponential, even though the map's Hessian is zero at the extremum. While these observations for the gradient algorithm are not trivial, we focus in this paper on an even more nontrivial study of the same phenomenon for Newton-based extremum seeking control (NESC). NESC is a second-order method which corrects for the unknown Hessian of the unknown map, not only in order to speed up parameter convergence, but also (1) to make the convergence rate user-assignable in spite of the unknown Hessian, and (2) to equalize the convergence rates in different directions for multivariable maps. Previous NESC work established stability only for maps whose Hessians are strictly positive definite everywhere, so the Hessian is invertible everywhere. For a scalar map, we establish the rather unexpected property that, even when the map behind is strictly convex but not strongly convex, i.e., when the Hessian may be zero, NESC guarantees practical asymptotic stability, semiglobally. While a model-based Newton-based algorithm would run into non-invertibility of the Hessian, the perturbation-based NESC, surprisingly, avoids this challenge by leveraging the fact that the average of the perturbation-based Hessian estimate is always positive, even though the actual Hessian may be zero., Comment: 6 pages, 5 figures

Over the last decade, data-driven methods have surged in popularity, emerging as valuable tools for control theory. As such, neural network approximations of control feedback laws, system dynamics, and even Lyapunov functions have attracted growing attention. With the ascent of learning based control, the need for accurate, fast, and easy-to-use benchmarks has increased. In this work, we present the first learning-based environment for boundary control of PDEs. In our benchmark, we introduce three foundational PDE problems - a 1D transport PDE, a 1D reaction-diffusion PDE, and a 2D Navier-Stokes PDE - whose solvers are bundled in an user-friendly reinforcement learning gym. With this gym, we then present the first set of model-free, reinforcement learning algorithms for solving this series of benchmark problems, achieving stability, although at a higher cost compared to model-based PDE backstepping. With the set of benchmark environments and detailed examples, this work significantly lowers the barrier to entry for learning-based PDE control - a topic largely unexplored by the data-driven control community. The entire benchmark is available on Github along with detailed documentation and the presented reinforcement learning models are open sourced., Comment: 26 pages 10 figures. Accepted L4DC 2024

This article provides an overview of the design of nonlinear feedback Cruise Controllers (CCs) for automated vehicles on lane-free roads. The feedback design problem is particularly challenging because of the various state constraints (e.g., collision-free movement, road geometry, speed limits) as well as the nature of the control objective (globally stabilizing distributed controllers that require measurements from neighboring vehicles only). Therefore, the resulting nonlinear control system is defined on an open set (not necessarily diffeomorphic to a linear space) for which the set of desired equilibria is non-compact. The proposed design of the CCs is based on energy-like control Lyapunov functions which combine potential functions with kinetic energy terms and other appropriate penalty terms. The feedback design in the microscopic level is accompanied by the derivation of the corresponding macroscopic traffic flow models. Explicit relations are established between selectable CC features and the obtained macroscopic traffic flow characteristics. This facilitates the active design of efficient traffic flow with desired properties, i.e., the construction of artificial traffic fluids., Comment: 53 pages

This study investigates the metacognitive capabilities of Large Language Models relative to human metacognition in the context of the International Coaching Federation ICF mimicking exam, a situational judgment test related to coaching competencies. Using a mixed method approach, we assessed the metacognitive performance, including sensitivity, accuracy in probabilistic predictions, and bias, of human participants and five advanced LLMs (GPT-4, Claude-3-Opus 3, Mistral Large, Llama 3, and Gemini 1.5 Pro). The results indicate that LLMs outperformed humans across all metacognitive metrics, particularly in terms of reduced overconfidence, compared to humans. However, both LLMs and humans showed less adaptability in ambiguous scenarios, adhering closely to predefined decision frameworks. The study suggests that Generative AI can effectively engage in human-like metacognitive processing without conscious awareness. Implications of the study are discussed in relation to development of AI simulators that scaffold cognitive and metacognitive aspects of mastering coaching competencies. More broadly, implications of these results are discussed in relation to development of metacognitive modules that lead towards more autonomous and intuitive AI systems.

Pairs of entangled photons are important for applications in quantum nanophotonics, where their theoretical description must accommodate their bipartite character. Such character is shared at the other end of the intensity range by, for example, the two degenerate instances of the pump field involved in second-harmonic generation. The description and numerical simulation of the interaction of nanophotonic structures with bipartite states of light is challenging regardless of their intensity, and has important technological applications. To address such a challenge, we develop here a theoretical and computational framework for studying the interaction of material structures with bipartite states of light. The theory of the framework rests on the symmetrized tensor product space of two copies of an electromagnetic Hilbert space. For the computational side, the convenient T-matrix method is extended to the tensor product space. When the response of the object to one part of the state is independent of the other part, the T-matrix for bipartite states is a simple function of the typical T-matrix of the single Hilbert space. Such separable material response is relevant, for example, in the interaction of entangled biphoton states with nanostructures. Non-separable operators are identified as the adequate objects to fully integrate non-linear effects such as sum frequency generation or parametric down-conversion. As an example of application, we derive selection rules for second-order non-linear processes in objects with rotational and/or mirror symmetries, and verify them numerically in two different MoS$_2$ clusters., Comment: 17 pages, 3 figures

Exploring novel strategies for the regulation of axon growth, we introduce a periodic event-triggered control (PETC) to enhance the practical implementation of the associated PDE backstepping control law. Neurological injuries may impair neuronal function, but therapies like Chondroitinase ABC (ChABC) have shown promise in improving axon elongation by influencing the extracellular matrix. This matrix, composed of extracellular macromolecules and minerals, regulates tubulin protein concentration, potentially aiding in neuronal recovery. The concentration and spatial distribution of tubulin influence axon elongation dynamics. Recent research explores feedback control strategies for this model, leading to the development of an event-triggering control (CETC) approach. In this approach, the control law updates when the monitored triggering condition is met, reducing actuation resource consumption. Through the meticulous redesign of the triggering mechanism, we introduce a periodic event-triggering control (PETC), updating control inputs at specific intervals, but evaluating the event-trigger only periodically, an ideal tool for standard time-sliced actuators like ChABC. PETC is a step forward to the design of practically feasible feedback laws for the neuron growth process. The PETC strategy establishes an upper bound on event triggers between periodic examinations, ensuring convergence and preventing Zeno behavior. Through Lyapunov analysis, we demonstrate the local exponential convergence of the system with the periodic event-triggering mechanism in the $L^2$-norm sense. Numerical examples are presented to confirm the theoretical findings., Comment: Submitted to 2024 Conference on Decision and Control

We propose a scheme for quantum-light generation in a nonlinear cavity hybridized with a 2-level system and theoretically show that, when excited by a series of controlled pump pulses, the hybrid source generates Fock states with high probabilities. E.g., 1- and 2-photon states can be generated near-on-demand, and Fock states with up to $7$ photons with a probability above $50\%$. The tailorable nature of the nonlinear cavity allows for generating Fock states with arbitrary frequencies, even with a fixed 2-level system, creating fundamentally new opportunities in all areas of quantum technologies.

We present Assignably Safe Extremum Seeking (ASfES), an algorithm designed to minimize a measured objective function while maintaining a measured metric of safety (a control barrier function or CBF) be positive in a practical sense. We ensure that for trajectories with safe initial conditions, the violation of safety can be made arbitrarily small with appropriately chosen design constants. We also guarantee an assignable ``attractivity'' rate: from unsafe initial conditions, the trajectories approach the safe set, in the sense of the measured CBF, at a rate no slower than a user-assigned rate. Similarly, from safe initial conditions, the trajectories approach the unsafe set, in the sense of the CBF, no faster than the assigned attractivity rate. The feature of assignable attractivity is not present in the semiglobal version of safe extremum seeking, where the semiglobality of convergence is achieved by slowing the adaptation. We also demonstrate local convergence of the parameter to a neighborhood of the minimum of the objective function constrained to the safe set. The ASfES algorithm and analysis are multivariable, but we also extend the algorithm to a Newton-Based ASfES scheme (NB-ASfES) which we show is only useful in the scalar case. The proven properties of the designs are illustrated through simulation examples.

This paper proposes a novel approach for locally stable convergence to Nash equilibrium in duopoly noncooperative games based on a distributed event-triggered control scheme. The proposed approach employs extremum seeking, with sinusoidal perturbation signals applied to estimate the Gradient (first derivative) of unknown quadratic payoff functions. This is the first instance of noncooperative games being tackled in a model-free fashion integrated with the event-triggered methodology. Each player evaluates independently the deviation between the corresponding current state variable and its last broadcasted value to update the player action, while they preserve control performance under limited bandwidth of the actuation paths and still guarantee stability for the closed-loop dynamics. In particular, the stability analysis is carried out using time-scaling technique, Lyapunov's direct method and averaging theory for discontinuous systems. We quantify the size of the ultimate small residual sets around the Nash equilibrium and illustrate the theoretical results numerically on an example.

A leading application of quantum computers is the efficient simulation of large unitary quantum systems. Extending this advantage to the study of open Cavity Quantum Electrodynamics (CQED) systems could enable the use of quantum computers in the exploration and design of many-body quantum optical devices. Such devices have promising applications in optical quantum communication, simulation, and computing. In this work, we present an early exploration of the potential for quantum computers to efficiently investigate open CQED physics. Our simulations make use of a recent quantum algorithm that maps the dynamics of a singly excited open Tavis-Cummings model containing $N$ atoms coupled to a lossy cavity. We report the results of executing this algorithm on two noisy intermediate-scale quantum computers, a superconducting processor and a trapped ion processor, to simulate the population dynamics of an open CQED system featuring $N = 3$ atoms. By applying technology-specific transpilation and error mitigation techniques, we minimize the impact of gate errors, noise, and decoherence in each hardware platform, obtaining results which agree closely with the exact solution of the system. These results provide confidence that future simulation algorithms, combined with emerging large-scale quantum processors, can be a powerful tool for studying cavity quantum electrodynamics., Comment: 10 pages, 15 figures

We consider the problem of inverse optimal control design for systems that are not affine in the control. In particular, we consider some classes of partial differential equations (PDEs) with quadratic convection and counter-convection, for which the L2 norm is a control Lyapunov function (CLF) whose derivative has either a depressed cubic or a quadratic dependence in the boundary control input. We also consider diffusive PDEs with or without linear convection, for which a weighted L2 norm is a CLF whose derivative has a quadratic dependence in the control input. For each structure on the derivative of the CLF, we achieve inverse optimality with respect to a meaningful cost functional. For the case where the derivative of the CLF has a depressed cubic dependence in the control, we construct a cost functional for which the unique minimizer is the unique real root of a cubic polynomial: the Cardano-Lyapunov controller. When the derivative of the CLF is quadratic in the control, we construct a cost functional that is minimized by two distinct feedback laws, that correspond to the two distinct real roots of a quadratic equation. We show how to switch from one root to the other to reduce the control effort., Comment: To Appear in Proceedings of the 2024 European Control Conference

In this work we advance the recently-introduced deep learning-powered approach to PDE backstepping control by proposing a method that approximates only the control gain function -- a function of one variable -- instead of the entire kernel function of the backstepping transformation, which depends on two variables. This idea is introduced using several benchmark unstable PDEs, including hyperbolic and parabolic types, and extended to 2X2 hyperbolic systems. By employing a backstepping transformation that utilizes the exact kernel (suitable for gain scheduling) rather than an approximated one (suitable for adaptive control), we alter the quantification of the approximation error. This leads to a significant simplification in the target system, shifting the perturbation due to approximation from the domain to the boundary condition. Despite the notable differences in the Lyapunov analysis, we are able to retain stability guarantees with this simplified approximation approach. Approximating only the control gain function simplifies the operator being approximated and the training of its neural approximation, potentially reducing the neural network size. The trade-off for these simplifications is a more intricate Lyapunov analysis, involving higher Sobolev spaces for some PDEs, and certain restrictions on initial conditions arising from these spaces. It is crucial to carefully consider the specific requirements and constraints of each problem to determine the most suitable approach; indeed, recent works have demonstrated successful applications of both full-kernel and gain-only approaches in adaptive control and gain scheduling contexts., Comment: Preprint submitted to CAM

We propose the first generalization of Sontag s universal controller to systems not affine in the control, particularly, to PDEs with boundary actuation. We assume that the system admits a control Lyapunov function (CLF) whose derivative, rather than being affine in the control, has either a depressed cubic, quadratic, or depressed quartic dependence on the control. For each case, a continuous universal controller that vanishes at the origin and achieves global exponential stability is derived. We prove our result in the context of convectionreaction-diffusion PDEs with Dirichlet actuation. We show that if the convection has a certain structure, then the L2 norm of the state is a CLF. In addition to generalizing Sontag s formula to some non-affine systems, we present the first general Lyapunov approach for boundary control of nonlinear PDEs. We illustrate our results via a numerical example., Comment: To be presented at the 2024 American Control Conference

There have been recent efforts that combine seemingly disparate methods, extremum seeking (ES) optimization and partial differential equation (PDE) backstepping, to address the problem of model-free optimization with PDE actuator dynamics. In contrast to prior PDE-compensating ES designs, which only guarantee local stability around the extremum, we introduce unbiased ES that compensates for delay and diffusion PDE dynamics while ensuring exponential and unbiased convergence to the optimum. Our method leverages exponentially decaying/growing signals within the modulation/demodulation stages and carefully selected design parameters. The stability analysis of our designs relies on a state transformation, infinite-dimensional averaging, local exponential stability of the averaged system, local stability of the transformed system, and local exponential stability of the original system. Numerical simulations are presented to demonstrate the efficacy of the developed designs., Comment: Submitted to the 63rd IEEE Conference on Decision and Control (CDC), 2024

Our recently developed "unbiased" extremum seeking (uES) algorithms ensure perfect convergence to the optimum at a user-assigned exponential rate or, more powerfully, within a user-prescribed time. Unlike classical approach, these algorithms use time-varying adaptation and controller gains, along with constant or time-varying probing frequencies (chirp signals). This paper advances our earlier uES designs from strongly convex maps with static optima to a broader class of convex cost functions with time-varying optima diverging at arbitrary rates, even in finite time. This advancement first motivates the use of Lie bracket averaging instead of classical averaging due to the average system system, which doesn't necessarily need to be exponentially convergent, and the existence of non-periodic time-varying parameters; second, it necessitates the formulation of non-trivial and key feasibility conditions for the choice of time-varying design parameters and their decay/growth rates in relation to the convexity of the map and the divergence rate of optima. These conditions indicate that, for constant-frequency probing, the user-defined asymptotic rate of unbiasing is limited by the convexity of the map. However, this rate can be made arbitrarily fast (including asymptotic, exponential, and prescribed time) using chirpy probing, which requires sufficiently rapid frequency and adaptation growth to enable tracking of faster-diverging optima. In addition to numerical simulations of the designs, we experimentally test the feasibility of exponential uES for tuning the angular velocity of a unicycle to seek a static light source., Comment: Submitted to Transactions on Automatic Control

In this paper, we extend our previous work on the power series method for computing backstepping kernels. Our first contribution is the development of initial steps towards a MATLAB toolbox dedicated to backstepping kernel computation. This toolbox would exploit MATLAB's linear algebra and sparse matrix manipulation features for enhanced efficiency; our initial findings show considerable improvements in computational speed with respect to the use of symbolical software without loss of precision at high orders. Additionally, we tackle limitations observed in our earlier work, such as slow convergence (due to oscillatory behaviors) and non-converging series (due to loss of analiticity at some singular points). To overcome these challenges, we introduce a technique that mitigates this behaviour by computing the expansion at different points, denoted as localized power series. This approach effectively navigates around singularities, and can also accelerates convergence by using more local approximations. Basic examples are provided to demonstrate these enhancements. Although this research is still ongoing, the significant potential and simplicity of the method already establish the power series approach as a viable and versatile solution for solving backstepping kernel equations, benefiting both novel and experienced practitioners in the field. We anticipate that these developments will be particularly beneficial in training the recently introduced neural operators that approximate backstepping kernels and gains., Comment: Preprint submitted to CDC 2024

Nonlinear convection, the source of turbulence in fluid flows, may hold the key to stabilizing turbulence by solving a specific cubic polynomial equation. We consider the incompressible Navier-Stokes equations in a two-dimensional channel. The tangential and normal velocities are assumed to be periodic in the streamwise direction. The pressure difference between the left and right ends of the channel is constant. Moreover, we consider no-slip boundary conditions, that is, zero tangential velocity, at the top and bottom walls of the channel, and normal velocity actuation at the top and bottom walls. We design the boundary control inputs to achieve global exponential stabilization, in the L2 sense, of a chosen Poiseuille equilibrium profile for an arbitrarily large Reynolds number. The key idea behind our approach is to select the boundary controllers such that they have zero spatial mean (to guarantee mass conservation) but non-zero spatial cubic mean. We reveal that, because of convection, the time derivative of the L2 energy of the regulation error is a cubic polynomial in the cubic mean of the boundary inputs. Regulation is then achieved by solving a specific cubic equation, using the Cardano root formula. The results are illustrated via a numerical example., Comment: To be presented at the 63rd IEEE Conference on Decision and Control (CDC 2024)

This study delves into the complex mechanisms of defect production and accumulation in nanocrystalline tungsten under irradiation, with a particular focus on the interplay between grain boundaries and free surfaces. Through molecular dynamics simulations, the research explores how grain boundaries act as sinks for irradiation-induced defects, a critical aspect in designing nanomaterials with enhanced radiation tolerance. The investigation leverages a novel Modified Wigner-Seitz Analysis to accurately quantify defect trends amidst dynamic surface reconstruction, providing a nuanced understanding of defect distribution in response to irradiation. This methodology underscores the intricate relationship between defect dynamics and the nano-scale structure of materials, specifically highlighting the role of interfaces in mediating these dynamics. The findings reveal a complex balance between defect production, surface interactions, and the influence of pre-existing defects and temperature on the primary defect production process. Surfaces are shown to amplify defect production due to biased accumulation of interstitials, alongside suppressed defect recombination, emphasizing the nuanced nature of defect dynamics in irradiated materials. This research contributes significantly to the fundamental understanding of defect formation and evolution in irradiated tungsten, offering insights that are instrumental in the development of nanomaterials poised for applications in extreme irradiation environments, such as fusion reactors, thereby advancing the field of materials science and engineering.

The existence of Quantum Many-Body Scars, which prevents thermalization from certain initial states after a long time, has been established across different quantum many-body systems. These include gauge theories corresponding to spin-1/2 quantum link models. Establishing quantum scars in gauge theories with high spin is not accessible with existing numerical methods, which rely on exact diagonalization. We systematically identify scars for pure gauge theories with arbitrarily large integer spin $S$ in $2+1$D, where the electric field is restricted to $2S+1$ states per link. Through an explicit analytic construction, we show that the presence of scars is widespread in $2+1$D gauge theories for arbitrary integer spin. We confirm these findings numerically for small truncated spin and $S=1$ quantum link models. Our analytic construction establishes the presence of scars far beyond volumes and spins that can be probed with existing numerical methods and can guide quantum simulation experiments toward interesting non-equilibrium phenomena, inaccessible otherwise., Comment: "Version 3 with an extended main text; 11 pages, 9 figures (including supplementary material)"

Second harmonic generation (SHG) is prohibited in centrosymmetric crystals such as silicon or germanium due to the presence of inversion symmetry. However, the structuring of such materials makes it possible to break the inversion symmetry, thus achieving generation of second-harmonic. Moreover, various symmetry properties of the resulting structure, such as chirality, also influence the SHG. In this work we investigate second harmonic generation from an array of nanohelices made of germanium. The intensity of the second harmonic displayed a remarkable enhancement of over 100 times compared to a non-structured Ge thin film, revealing the influence of interaction between nanohelices. In particular, nonlinear circular dichroism, characterized through the SHG anisotropy factor g, changed its sign not only with the helix handedness, but with its density as well. We believe that our discoveries will open up new paths for the development of nonlinear photonics based on metamaterials and metasurfaces made of centrosymmetric materials., Comment: 16 pages, 5 figures

In the last years, Infrared Reflection-Absorption Spectroscopy (IRRAS) became a standard technique to study vibrational excitations of molecules. These investigations are strongly motivated by perspective applications in monitoring chemical processes. For a better understanding of the adsorption mechanism of molecules on dielectrics, the polarization-dependence of an interaction of infrared light with adsorbates at dielectric surfaces is commonly used. Thus, the peak positions in absorption spectra could be different for s- and p-polarized light. This shift between the peak positions depends on both the molecule itself and the dielectric substrate. While the origin of this shift is well understood for infinite two-dimensional adsorbate layers, finite-size samples, which consist of 2D islands of a small number of molecules, have never been considered. Here, we present a study on polarization-dependent finite-size effects in the optical response of such islands on dielectric substrates. The study uses a multi-scale modeling approach that connects quantum chemistry calculations to Maxwell scattering simulations. We distinguish the optical response of a single molecule, a finite number of molecules, and a two-dimensional adsorbate layer. We analyze CO and CO$_2$ molecules deposited on CeO$_2$ and Al$_2$O$_3$ substrates. The evolution of the shift between the polarization-dependent absorbance peaks is firstly studied for a single molecule, which it does not exhibit for at all, and for finite molecular islands, which it increases with increasing island size for, as well as for an infinite two-dimensional adsorbate layer. In the latter case, the agreement between the obtained results and the experimental IRRAS data and more traditional three/four-layer-model theoretical studies supports the predictive power of the multi-scale approach.

While Privacy by Design (PbD) is prescribed by modern privacy regulations such as the EU's GDPR, achieving PbD in real software systems is a notoriously difficult task. One emerging technique to realize PbD is Runtime enforcement (RE), in which an enforcer, loaded with a specification of a system's privacy requirements, observes the actions performed by the system and instructs it to perform actions that will ensure compliance with these requirements at all times. To be able to use RE techniques for PbD, privacy regulations first need to be translated into an enforceable specification. In this paper, we report on our ongoing work in formalizing the GDPR. We first present a set of requirements and an iterative methodology for creating enforceable formal specifications of legal provisions. Then, we report on a preliminary case study in which we used our methodology to derive an enforceable specification of part of the GDPR. Our case study suggests that our methodology can be effectively used to develop accurate enforceable specifications.

In this paper we extend our recently proposed Deadzone-Adapted Disturbance Suppression (DADS) Control approach from systems with matched uncertainties to general systems in parametric strict feedback form. The DADS approach prevents gain and state drift regardless of the size of the disturbance and unknown parameter and achieves an attenuation of the plant output to an assignable small level, despite the presence of persistent disturbances and unknown parameters of arbitrary and unknown bounds. The controller is designed by means of a step-by-step backstepping procedure which can be applied in an algorithmic fashion. Examples are provided which illustrate the efficiency of the DADS controller compared to existing adaptive control schemes., Comment: 32 pages, 6 figures. arXiv admin note: text overlap with arXiv:2311.07938

This paper introduces extremum seeking (ES) algorithms designed to achieve perfect tracking of arbitrary time-varying extremum. In contrast to classical ES approaches that employ constant frequencies and controller gains, our algorithms leverage time-varying parameters, growing either asymptotically or exponentially, to achieve desired convergence behaviors. Our stability analysis involves state transformation, time-dilation transformation, and Lie bracket averaging. The state transformation is based on the multiplication of the input state by asymptotic or exponential growth functions. The time transformation enables tracking of the extremum as it gradually converges to a constant value when viewed in the dilated time domain. Finally, Lie bracket averaging is applied to the transformed system, ensuring practical uniform stability in the dilated time domain as well as asymptotic or exponential stability of the original system in the original time domain. We validate the feasibility of these designs through numerical simulations., Comment: Accepted to the 2024 American Control Conference (ACC)

State-of-the-art algorithms in lattice gauge theory typically rely heavily on detailed balance, which is an instrumental tool to prove the correct convergence of the Markov Chain Monte Carlo Algorithm. In this work, we investigate an alternative to detailed balance, skewed detailed balance, and the prospects of alleviating the topological freezing problem by studying the one-dimensional $O\left(2\right)$ model., Comment: PoS Lattice 2022

State-of-the-art algorithms for simulating fermions coupled to gauge fields often rely on integrating fermion degrees of freedom. While successful in simulating QCD at zero chemical potential, at finite density these approaches are hindered by the sign problem. We discuss the simulation of lattice gauge theories in the Hamiltonian formalism and present a generalized meron-cluster algorithm for the simulation of the $U\left(1\right)$ Quantum Link Model for spin $1/2$. This enables the study of models directly relevant to current quantum simulators and is a promising first step toward constructing new efficient algorithms for more complicated gauge theories., Comment: PoS Lattice 2023

In large enough volumes, translation-averaging for quark-line connected diagrams reduces the variance inversely proportional to the volume. Stochastic estimators which implement translation averaging however introduce new sources of fluctuations, which in some cases can be relatively large. In this work, we explore whether inexact deflation subspaces can be used to improve the precision of the isovector vector correlators. We perform numerical experiments with $N_\mathrm{f}=2$ non-perturbatively $\mathrm O(a)$-improved Wilson fermions and measure the relative contribution from the deflation subspace to the central value and the corresponding variance.

The chirality of an object can be studied by measuring the circular dichroism, that is, the difference in absorption of light with different helicity. The chiral optical response of an object, however, can have two different origins. On the one hand, it can be linked to the chiral geometry of the object. On the other hand, it can be linked to the chiral material from which the object is made. Whereas previously, no distinction between the two contributions could be made, we report here a computational approach that allows us to separate these two contributions to the circular dichroism of an object. We consider separately the cases where geometry-related resonances affect the optical response and where they are absent. In both cases, we find the circular dichroism to be easily decomposable if a geometrically achiral object has a similar absorption spectrum to the chiral object under investigation. Furthermore, in the non-resonant case, the contribution attributed to the material can be obtained without taking any geometry into account. Besides being of fundamental importance, the possibility of disentangling both contributions will be important for guiding the future design of chiral objects and devices.

Observers for PDEs are themselves PDEs. Therefore, producing real time estimates with such observers is computationally burdensome. For both finite-dimensional and ODE systems, moving-horizon estimators (MHE) are operators whose output is the state estimate, while their inputs are the initial state estimate at the beginning of the horizon as well as the measured output and input signals over the moving time horizon. In this paper we introduce MHEs for PDEs which remove the need for a numerical solution of an observer PDE in real time. We accomplish this using the PDE backstepping method which, for certain classes of both hyperbolic and parabolic PDEs, produces moving-horizon state estimates explicitly. Precisely, to explicitly produce the state estimates, we employ a backstepping transformation of a hard-to-solve observer PDE into a target observer PDE, which is explicitly solvable. The MHEs we propose are not new observer designs but simply the explicit MHE realizations, over a moving horizon of arbitrary length, of the existing backstepping observers. Our PDE MHEs lack the optimality of the MHEs that arose as duals of MPC, but they are given explicitly, even for PDEs. In the paper we provide explicit formulae for MHEs for both hyperbolic and parabolic PDEs, as well as simulation results that illustrate theoretically guaranteed convergence of the MHEs., Comment: 7 pages, 1 figure, submitted to ACC 2024

To stabilize PDE models, control laws require space-dependent functional gains mapped by nonlinear operators from the PDE functional coefficients. When a PDE is nonlinear and its "pseudo-coefficient" functions are state-dependent, a gain-scheduling (GS) nonlinear design is the simplest approach to the design of nonlinear feedback. The GS version of PDE backstepping employs gains obtained by solving a PDE at each value of the state. Performing such PDE computations in real time may be prohibitive. The recently introduced neural operators (NO) can be trained to produce the gain functions, rapidly in real time, for each state value, without requiring a PDE solution. In this paper we introduce NOs for GS-PDE backstepping. GS controllers act on the premise that the state change is slow and, as a result, guarantee only local stability, even for ODEs. We establish local stabilization of hyperbolic PDEs with nonlinear recirculation using both a "full-kernel" approach and the "gain-only" approach to gain operator approximation. Numerical simulations illustrate stabilization and demonstrate speedup by three orders of magnitude over traditional PDE gain-scheduling. Code (Github) for the numerical implementation is published to enable exploration., Comment: 16 pages, 5 figures

We present multivariable extremum seeking (ES) designs that achieve unbiased convergence to the optimum. Two designs are introduced: one with exponential unbiased convergence (unbiased extremum seeker, uES) and the other with user-assignable prescribed-time unbiased convergence (unbiased PT extremum seeker, uPT-ES). In contrast to the conventional ES, which uses persistent sinusoids and results in steady-state oscillations around the optimum, the exponential uES employs an exponentially decaying amplitude in the perturbation signal (for achieving convergence) and an exponentially growing demodulation signal (for making the convergence unbiased). The achievement of unbiased convergence also entails employing an adaptation gain that is sufficiently large in relation to the decay rate of the perturbation amplitude. Stated concisely, the bias is eliminated by having the learning process outpace the waning of the perturbation. The other algorithm, uPT-ES, employs prescribed-time convergent/blow-up functions in place of constant amplitudes of sinusoids, and it also replaces constant-frequency sinusoids with chirp signals whose frequency grows over time. Among the convergence results in the ES literature, uPT-ES may be the strongest yet in terms of the convergence rate (prescribed-time) and accuracy (unbiased). To enhance the robustness of uES to a time-varying optimum, exponential functions are modified to keep oscillations at steady state. Stability analysis of the designs is based on a state transformation, averaging, local exponential/PT stability of the averaged system, local stability of the transformed system, and local exponential/PT stability of the original system. For numerical implementation of the developed ES schemes and comparison with previous ES designs, the problem of source seeking by a two-dimensional velocity-actuated point mass is considered., Comment: 16 pages, 7 figures

We present results for tetraquark potentials of two static anti-quarks $\bar b \bar b$ in the presence of two light quarks $u$ and/or $d$. We improve on existing results by computing the static potential also for off-axis separations, which increases the number of data points significantly. Moreover, we compute for the first time $\bar b \bar b u s$ potentials., Comment: 8 pages, 3 figures, 1 table, parallel talk given at the 40th International Symposium on Lattice Field Theory (Lattice2023), July 31st - August 4th 2023, Fermi National Accelerator Laboratory, Naperville, Illinois, USA

Deep neural network approximation of nonlinear operators, commonly referred to as DeepONet, has proven capable of approximating PDE backstepping designs in which a single Goursat-form PDE governs a single feedback gain function. In boundary control of coupled PDEs, coupled Goursat-form PDEs govern two or more gain kernels-a PDE structure unaddressed thus far with DeepONet. In this paper, we explore the subject of approximating systems of gain kernel PDEs for hyperbolic PDE plants by considering a simple counter-convecting $2\times 2$ coupled system in whose control a $2\times 2$ kernel PDE system in Goursat form arises. Engineering applications include oil drilling, the Saint-Venant model of shallow water waves, and the Aw-Rascle-Zhang model of stop-and-go instability in congested traffic flow. We establish the continuity of the mapping from a total of five plant PDE functional coefficients to the kernel PDE solutions, prove the existence of an arbitrarily close DeepONet approximation to the kernel PDEs, and ensure that the DeepONet-approximated gains guarantee stabilization when replacing the exact backstepping gain kernels. Taking into account anti-collocated boundary actuation and sensing, our $L^2$-Globally-exponentially stabilizing (GES) approximate gain kernel-based output feedback design implies the deep learning of both the controller's and the observer's gains. Moreover, the encoding of the output-feedback law into DeepONet ensures semi-global practical exponential stability (SG-PES). The DeepONet operator speeds up the computation of the controller gains by multiple orders of magnitude. Its theoretically proven stabilizing capability is demonstrated through simulations.

This paper proposes an event-triggered control scheme for multivariable extremum seeking of static maps. Both static and dynamic triggering conditions are developed. Integrating Lyapunov and averaging theories for discontinuous systems, a systematic design procedure and stability analysis are developed. Both event-based methods enable one to achieve an asymptotic stability result. Ultimately, the resulting closed-loop dynamics demonstrates the advantages of combining both approaches, namely, event-triggered control and extremum seeking. Although we keep the presentation using the classical event-triggered method, the extension of the results for the periodic event-triggered approach is also indicated. An illustration of the benefits of the new control method is presented using consistent simulation results, which compare the static and the dynamic triggering approaches., Comment: 21 pages, 6 figures, and 1 table