Papers

We solve the four-dimensional Hamilton-Jacobi-Bellman (HJB) equation for two diffusively coupled Stuart-Landau-like oscillators to obtain full-state optimal feedback control. A sweep over coupling strength reveals a sharp change in the numerically optimal strategy: below a threshold coupling value, the controller steers the phase difference toward anti-phase while keeping both oscillators near the limit cycle, as reduced-order models would suggest. Above this threshold, the HJB solution changes qualitatively; the controller transiently collapses one oscillator's amplitude to near zero, thereby enabling large phase repositioning near the origin before rebuilding its amplitude. Direct gradient-based and stochastic optimization do not recover this lower-cost collapse trajectory from the initializations considered, suggesting that it occupies a region of the control landscape that is difficult to access by direct search. A joint sweep over nonisochronicity and coupling shows that collapse can occur even for an isochronous oscillator: phase repositioning near the origin can favor an off-cycle strategy. Nonisochronicity provides an additional energetic benefit through a phase-velocity surplus at small amplitude, quantitatively accounting for the observed reduction in control cost. Comparisons with uncoupled and coupled phase-reduced baselines show that phase models become increasingly inaccurate and cost significantly more energy for strong coupling. Results for coupled Van der Pol oscillators further demonstrate that exploitation of off-cycle dynamics is not specific to the Stuart-Landau-like oscillators.

Unmanned aerial vehicle (UAV) swarms deployed in mission-critical applications must simultaneously track a mobile aerial target and maintain reliable data links. However, active integrated sensing and communication (ISAC) operation imposes a dual energy burden on propulsion and transmission, threatening mission continuity through premature battery depletion. In this paper, we propose a two-tier UAV swarm architecture in which mission UAVs (MUAVs) execute cooperative ISAC for mobile aerial target tracking while dedicated charging UAVs (CUAVs), equipped with solar harvesting panels, replenish low-battery MUAVs via aerial UAV-to-UAV wireless power transfer (WPT). We formulate the joint minimization of the cooperative posterior Cramér-Rao bound (PCRB) over MUAV trajectories, per-slot sensing-communication time splits, WPT scheduling and admission, and CUAV rendezvous trajectories, subject to minimum uplink rate, dual-tier energy causality, WPT proximity, collision-avoidance, and speed constraints, yielding a non-convex mixed-integer program (MIP) that, to the best of our knowledge, is the first to jointly couple cooperative ISAC sensing quality with aerial WPT and dual-tier energy management. To solve it efficiently, we propose Receding-Horizon Alternating Optimization (RHAO), a four-block per-slot algorithm that decomposes the problem into: charging admission via the Hungarian algorithm, MUAV trajectory and time-split via successive convex approximation (SCA), CUAV rendezvous, and WPT power allocation, with monotone convergence guarantees. Simulation results demonstrate that RHAO reduces the mean PCRB by 16.8 times over a fixed-time-split baseline and 5.4 times over a static-trajectory scheme, while the aerial WPT subsystem sustains all MUAVs above the energy-critical threshold throughout the full mission horizon.

Wireless edge caching networks typically consist of many independent Base Stations (BSs), each facing its own request rate and content popularity profile. Training a Reinforcement Learning (RL) caching agent from scratch at every BS forces each agent to relearn, through slow trial and error, a decision problem that is structurally identical across the network. Meta-reinforcement learning removes this redundancy by learning a shared initialization that adapts to any BS in a few local updates; however, meta-training itself becomes the bottleneck at scale: the meta-gradient must be estimated from a small subset of BSs at each meta-iteration, and sampling this subset uniformly at random yields a high-variance estimate, an issue existing meta-RL caching frameworks leave unaddressed. This paper proposes a meta-reinforcement learning framework for caching across independent, non-overlapping BSs that directly targets this bottleneck. Each BS runs a local Proximal Policy Optimization (PPO) agent, formulated as a Semi-Markov Decision Process (SMDP) over content popularity, size, lifetime, and importance, while a shared meta-policy is learned via a Model-Agnostic Meta-Learning (MAML)-style loop. To scale meta-training and accelerate convergence, we introduce gradient-based clustering, which groups BSs by local gradient similarity and draws from every cluster, in proportion to its size, at each meta-iteration. We prove, via an Analysis of Variance (ANOVA)-style decomposition of gradient variance, that this strategy yields a strictly lower-variance meta-gradient estimator than uniform random sampling under BS heterogeneity.

Precise manipulation of liquid droplets underpins lab-on-a-chip platforms for diagnostics, chemical synthesis, and biological assays. Yet autonomous droplet transport through confined geometries of varying complexity remains an open challenge. Droplets exhibit contact-angle hysteresis, deformability, and capillary pinning, which make their response to actuation nonlinear and history dependent, that classical controllers and pre-programmed trajectories cannot cope in multi-turn environments. Here we demonstrate autonomous navigation of a liquid droplet through geometries of increasing complexity on a gravity driven (Labyrinth) platform using model-based reinforcement learning. A thin silicone oil film reduces contact-line pinning while two-axis tilt supplies the gravitational driving force, and an overhead camera tracks the droplet in real time. An offline-trained policy discovers effective tilt strategies from limited physical interaction data, without simulation or analytical droplet models. The system operates under partial observability, as oil-film thickness, instantaneous contact angle, and droplet deformation state remain hidden from the controller. Despite these challenges, the learned policy achieves reliable navigation across straight, right-angle, and curved-arc paths, including outside-corner geometries. We further demonstrate that a policy trained on a simpler geometry transfers to complex ones, succeeding zero-shot on right-angle and staircase paths and reaching full success on a curved arc with a fifth of the training data. The findings suggest promising avenues for enabling droplet based microfluidic systems to serve as intelligent chemical laboratories.

Electromagnetic navigation requires sufficient actuation at clinically relevant distances due to limited magnetic volumes and coil currents. We combine real-time pose feedback with constrained convex optimization, dynamic feedback, and repetitive control to achieve energy-efficient micro-guidewire steering inside realistic anatomical models. Experiments with a clinically oriented, three-coil electromagnetic navigation system and a 0.6 mm-diameter tip magnet demonstrate angular tracking with root-mean-square errors below 0.25 degrees at distances up to 55 cm from the coil cover. Nullspace current redistribution maintains accurate tracking under active 45 A coil-current constraints. Compared with conventional field alignment, we demonstrate that pose-dependent torque-based allocation substantially reduces current demand, with the efficiency benefit retained at a pose-feedback rate of 15 Hz. These results demonstrate how real-time state information and optimization can extend electromagnetic guidewire control toward clinically relevant working distances.

Operator splitting methods are a fundamental class of algorithms for solving structured monotone inclusion problems arising in optimization, control, and game theory. We consider the case, common in stochastic regimes, where the forward evaluation of one of the constituent operators is either unavailable in closed form or computationally expensive to evaluate, and is therefore approximated using a finite number of noisy oracle samples. We establish distribution-free, finite-sample certificates for the quality of the output produced by data-driven Davis-Yin splitting algorithms. Unlike previous works, our analysis directly controls the residual error via martingale inequalities instead of relying on algorithmic stability arguments for a tailored surrogate loss, yielding the first a priori certificates whose statistical excess provably vanishes with the sample size. We further show that, under linear convergence of the Davis-Yin splitting algorithm, the dependence of our bounds on the iteration count improves from linear growth to exponential decay. We validate our theoretical results on a stochastic portfolio optimization problem with uncertain asset returns.

Domain Randomization (DR) has been widely used to overcome the sim-to-real gap by training a controller on a distribution of simulated environments via reinforcement learning. While DR can achieve robust performance simply using controllers synthesized via policy gradient (PG) methods, the optimization landscape is not well understood, even in the case of linear quadratic regulator (LQR) objectives. To this end, we first study PG of domain randomized LQR over history-dependent policy classes, such as finite impulse response controllers, as they can extend the possibilities of simultaneous stabilization. Second, to find such a stabilizing controller, we propose a curriculum learning based algorithm which gradually expands the memory of the controller. Finally, we show that PG with the proposed algorithm converges globally to the minimizer of a sample average approximation of the DR objective under suitable bounds on the heterogeneity of environments. Empirical results support our findings and highlight promising directions for future work, including nonlinear domain-randomized control.

The ongoing replacement of synchronous generation by inverter-based resources (IBRs) introduces fast converter control dynamics whose characteristic frequencies extend beyond the classical electromechanical band. Conventional small-signal stability tools are commonly formulated in the phasor domain, representing electrical quantities as slowly varying phasors at the fundamental frequency, and therefore cannot resolve the sub-synchronous and converter-driven dynamics that increasingly arise in operation. Electromagnetic Transient (EMT) modeling captures these dynamics, but established EMT simulators produce time-domain waveforms rather than the modal indicators (eigenvalues, damping ratios, and participation factors) needed to assess stability risk. This paper presents EMT-SSA, an integrated tool suite for EMT-level small-signal analysis of IBR-rich power systems. From a standard PSS/E system snapshot (.raw/.dyr), it converts the model into an EMT representation with enhanced fidelity, solves an extended power flow for the steady-state equilibrium points of both the network and the device controllers, and linearizes a full-order EMT model to form the system state matrix, from which it produces eigenvalues, oscillation modes, and participation factor analysis results over a device library spanning synchronous generators, grid-following and grid-forming inverters, transmission lines, and loads. The EMT-SSA tool suite identifies poorly damped or unstable modes and attributes them to specific devices. The tool is demonstrated on the Kundur two-area system, with its modal results benchmarked against PSS/E NEVA.

Continuum robots enable smooth shape morphing and safe interaction in confined environments. However, most existing systems are task-specific and depend on external sensing infrastructure, limiting their adaptability and real-world deployment. This paper presents a self-contained modular continuum robotic platform that combines mechanical reconfigurability with onboard pose estimation. The robot is constructed from interchangeable continuum joints with analytically precomputed stiffness, allowing rapid assembly and direct programming of the robot shape. Proprioceptive sensing is achieved using magnetic sensors and a modular learning-based framework, where a single model is trained per joint and reused across configurations. The system is experimentally validated in real world, demonstrating self-sensing capabilities and adaptation without external tracking.

The sharp increase in mass shootings underscores an urgent need for systems that guide victims to safety in real time. An effective evacuation system must minimize threat exposure while also accounting for adversarial uncertainty and crowding dynamics. Current methods in the literature are rigidly constrained to layout-specific policies and computationally intractable in large-scale layouts, while practical guidelines simply advise victims to "run", "hide", or "fight". We propose GPEvac: a GNN-based PPO framework that computes adaptive evacuation routes during shooting events. To capture both local and long-distance dependencies, we introduce an edge-first sequential message-passing scheme with a learnable virtual global node. The resulting graph embeddings are integrated into a permutation-invariant scoring mechanism that allows a single learned policy to operate across building layouts of diverse topologies and sizes. Through extensive simulation, we show that GPEvac outperforms intelligent baselines across distinct architectural layouts, significantly reducing total threat exposure. Crucially, the system computes global evacuation routes in just 14.73 ms on local CPU hardware, enabling seamless integration with live surveillance systems. In addition to saving lives during shooting events, the methodologies developed are transferable to other graph-structured decision-making domains, including critical infrastructure, intelligent transportation systems, and adaptive sensor networks.

Meta-reinforcement learning (meta-RL) enables agents to adapt to unseen tasks with limited experience. Despite its promise, the application of meta-RL in real-world tasks is hindered by safety requirements, which have been underexplored in prior work. In this paper, we propose a safe meta-RL framework that explicitly accounts for safety during adaptation. Our key insight is to reason about safety in the information space, which captures both the physical state and the agent's belief over the underlying task. Within this space, we introduce a safety value function that measures the probability of the agent avoiding unsafe regions indefinitely. We show that this function satisfies a self-consistency condition and a Bellman equation, which make it learnable via meta-RL. Based on this formulation, we develop a safe meta-RL algorithm that learns the safety value function and leverages it for safety filtering and constrained policy optimization. Experiments on meta-RL benchmarks demonstrate the effectiveness of the proposed method.

Hydrogen-diesel dual-fuel (H2DF) combustion reduces combustion-out CO2 emissions but exhibits nonlinear cycle-to-cycle dynamics at high hydrogen energy shares (HES). This work evaluates nonlinear model predictive control (NMPC) with a gated recurrent-unit deep neural network dynamics model for transient H2DF control. Trained on 99,800 engine cycles, the model predicts indicated mean effective pressure, nitrogen oxides (NOx), particulate matter (PM), and maximum pressure-rise rate. Single-cylinder Cummins 4.5 L experiments follow an unseen 4,900-engine-cycle trajectory. Compared with production diesel-only control, NMPC improves load-tracking mean absolute error by 27.8% and reduces mean PM by 61.1%, while mean engine-out NOx increases by 105.1% without exhaust-gas recirculation. Mean and peak HES reach 39.7% and 55.1%; a high-hydrogen setting achieves 77.8% peak HES without constraint violations. Robust to feedback noise and model-plant mismatch and executing in 3 to 7 ms per engine cycle on low-cost embedded hardware, learned-dynamics NMPC enables practical, real-time, constraint-aware transient H2DF control with substantial diesel substitution.

Proximal bundle methods (PBMs) are classical algorithms for nonsmooth convex optimization. Existing analyses of the classical PBM couple the null steps with the descent test. This coupling obscures how the bundle updates approximate the proximal subproblem. In this work, we consider composite objectives $F=f+h$ and view each null-step cycle as an inner bundle subroutine, called $\mathtt{ProxBundle}$. We analyze $\mathtt{ProxBundle}$ independently of any stopping criterion under general bundle model conditions and show that it automatically adapts to Hölder smoothness. Combining this inner-loop analysis with the descent-step analysis yields sharper complexity bounds for the classical PBM. For any fixed proximal parameter, its overall complexity is $\mathcal O\big(\epsilon^{-\frac{3-\nu}{1+\nu}}\big)$ for $\nu\in[0,1)$ and $\mathcal O(\epsilon^{-1})$ for $\nu=1$, where $\nu$ is the Hölder smoothness exponent. Choosing the proximal parameter proportional to $\epsilon$ improves the rate to $\mathcal O\big(\epsilon^{-\frac{2}{1+\nu}}\big)$ for $\nu \in [0,1)$. These are the first guarantees under Hölder smoothness with $\nu\in(0,1)$ for the classical descent test. We further introduce an absolute model-error test. The resulting PBM variant admits a clean inexact proximal-point analysis, and for any fixed proximal parameter, achieves the same complexity $\mathcal O\big(\epsilon^{-\frac{2}{1+\nu}}\big)$. Overall, our analysis separates the roles of the descent and null steps and gives a modular understanding of PBMs across different~tests.

This paper studies the strategic market participation of a monopolistic energy storage aggregator (ESA) in a day-ahead electricity market. The ESA coordinates geographically distributed storage units, submits a coordinated bid for its portfolio, and may hold financial transmission rights (FTRs). The system operator clears the market through a network-constrained, multi-period economic dispatch, determining generation and load schedules, nodal prices, energy-market payments, and FTR payoffs. We formulate the ESA–system-operator interaction as a Stackelberg game and characterize its equilibrium through a constraint-binding-pattern decomposition of the market-clearing problem. Beyond enabling equilibrium computation, the framework reveals how the ESA can increase its profit by strategically inducing or avoiding particular constraint-binding patterns. It also establishes a novel welfare result: although strategic storage without FTRs is known to weakly improve social welfare relative to the no-storage case, certain FTR positions can overturn this guarantee by strengthening the ESA's incentive to induce particular patterns, causing social welfare to fall below the no-storage level. Motivated by these findings, we develop two system-operator mechanisms for limiting undesirable ESA behavior and its adverse effects on market outcomes and social welfare. Finally, a three-bus study illustrates the theoretical findings, while IEEE test systems demonstrate the scalability of the proposed method.

Industrial PID loops are still tuned by hand: step the setpoint, look at the response, change a gain. This paper turns that procedure into an algorithm, SPIN, for Step-response Phase-portrait INspection. From a single closed-loop step response we form three phase portraits of the control deviation and count how many times each trajectory winds around its settling point. The counts are dimensionless and independent of the step size and of the clock. They show which channel is under-damped: integral, proportional, or derivative. A triangular rule reads the three counts, cutting the gain of the one band whose fault is certain and raising that of every band the same test shows to be within its limit. No model is identified and no optimization is solved. The iteration is deterministic and its gains stay inside a fixed box. Because a ringing count is itself the evidence that the loop is in the regime where cutting that band's gain reduces the count, the iteration settles into a cycle at the edge of the well-damped set. It needs no excitation beyond a routine setpoint step, so it can run on a process in operation, and it may start from arbitrary gains, including destabilizing ones. SPIN assumes a stable, self-regulating process whose gain and phase both fall with frequency, which covers the lag-plus-dead-time models common in process control, and a controller from the nested family $\mathrm{I} \subset \mathrm{PI} \subset \mathrm{PID}$ with integral action present. We validate it on a battery of process-typical plants with one fixed step size and one set of dimensionless constants throughout. Every decision is visible: the operator sees what the algorithm sees. A reference implementation and an interactive demonstration are public.

Learning Model Predictive Control (LMPC) improves iterative control tasks by using previous executions to construct the terminal constraint and terminal cost of the MPC problem. Although effective, this reuse of past trajectories can make LMPC sensitive to the initial data. In particular, LMPC may repeatedly exploit stored trajectories with favorable cost-to-go values while insufficiently exploring alternative route patterns that could yield lower cost after further improvement. To address this issue, we propose Multi-Modal LMPC (MM-LMPC). The proposed framework clusters past trajectories into motion modes, constructs a mode-specific LMPC controller for each mode, and uses an LCB-based meta-controller to select which mode-specific controller to execute at each iteration. Mode information is incorporated into the terminal constraint and terminal cost through two designs. The hard-constrained design uses mode-specific terminal constraints and terminal costs constructed from the data associated with each mode. The soft-regularized design retains a shared terminal constraint while adding membership-based penalties to the terminal cost. These designs reduce the bias caused by pooling all trajectories into a single terminal memory while retaining the recursive feasibility and stability structure of LMPC. Our theoretical analysis shows that both designs preserve recursive feasibility and closed-loop stability. For the hard-constrained design, we further establish mode-wise cost convergence, asymptotic best-mode performance, and a logarithmic cumulative regret bound under the LCB rule. Simulations on multi-route obstacle-avoidance tasks show that MM-LMPC improves exploration and achieves lower costs than standard LMPC.

Estimation of the derivatives of a function with bounded high-order derivative in the presence of bounded measurement noise is considered, in a deterministic setting. Theoretical fundamental limitations of causal differentiators in terms of the lowest achievable worst-case differentiation error and desired properties such as exactness—the zero-error estimation of derivatives in the absence of noise—and robustness—the small sensitivity of the estimate under small perturbations—are established for the first time for arbitrary differentiation order. Differentiators that achieve these theoretically lowest bounds on their differentiation error are formally defined, fully characterized, and their properties are studied. In particular, deterministically optimal differentiators—those featuring optimal differentiation error bounds among the class of exact differentiators—are shown to exist by means of a novel construction exhibiting, in addition to exactness from the beginning, a very strong form of robustness.

This work presents a delayed safe Newton-based extremum seeking (SANES) framework for minimizing an unknown static map subject to an unknown safety constraint. The objective and safety measurements are assumed to be affected by the same constant time delay. To compensate for delayed measurements, a model-free predictor is developed to construct the quantities required for optimization, including the nominal Newton-based extremum-seeking control input and the gradient information used to formulate control Lyapunov function (CLF) and control barrier function (CBF) conditions. Robust CLF–CBF quadratic programs (QPs), subject to parameter-update constraints, are then formulated to account explicitly for derivative-estimation and prediction errors. For the delay-free case, robustness margins are derived from bounds on the extremum-seeking estimation errors, whereas for the delayed case, the margins incorporate both estimation and prediction errors. Practical stability of the nominal Newton-based extremum-seeking dynamics is established directly through a Lyapunov analysis, thereby providing convergence guarantees over the admissible parameter set without relying exclusively on local averaging arguments. Robust CLF and CBF conditions are subsequently derived to establish practical convergence and forward invariance of a robust subset of the prescribed safe set. A numerical case study demonstrates the effectiveness of the proposed SANES framework in achieving constrained optimization despite unknown objective and safety maps and delayed measurements.

Optimizing the sensitivity of the unbalance voltage in Wheatstone bridges with respect to changes in the bridge parameters remains a fundamental objective in circuit design and instrumentation. When finite source and detector resistances are taken into account, determining the optimal bridge configuration becomes increasingly complex, and a closed-form analytical representation of the optimal solution has not yet been established. This paper derives an analytical solution for the optimal configuration of a Wheatstone bridge with finite source and detector resistances. Furthermore, the proposed optimal solution is compared with the conventional equal-arm configuration.

The proliferation of distributed energy resources is increasing the prevalence of both overvoltages and undervoltages in low-voltage (LV) distribution networks. Smart inverter functionalities, such as Volt-VAr and Volt-Watt control, can regulate voltage at the consumer level but are challenging to capture in optimization models due to their nondifferentiability. This paper proposes three nonlinear models for four-wire unbalanced optimal power flow that incorporate these nonsmooth functions without binary or integer variables. The first model encodes these nonsmooth functions directly as user-defined functions using control flow, a feat enabled by many state-of-the-art algebraic modeling languages. The second and third introduce bespoke smooth approximations with tunable approximation errors to address potential numerical issues arising from nondifferentiability, improving reliability while maintaining accuracy. All three methods are evaluated on real four-wire unbalanced LV networks with varying rooftop solar adoption levels, including a 539-bus system with 302 smart inverters. This paper is not only the first to demonstrate accurate, reliable, and tractable Volt-Var-Watt optimization (VVWO) on real four-wire unbalanced LV network models, but it also establishes that optimization-based, non-incremental methods offer superior reliability compared to commonly used incremental (quasi-steady-state) approaches.

As networked dynamical systems grow in scale, distributed controller architecture design has become a critical issue. To address this, prior studies have incorporated the regularization for design (RFD) framework into system-level synthesis (SLS), focusing primarily on actuator and sensor placement. However, communication architectures also fundamentally impact the performance of distributed control, and scalable explicit co-design of the underlying communication topology within SLS remains challenging. This paper proposes a scalable convex framework for the co-design of communication architectures and distributed controllers within the SLS paradigm. We explicitly characterize a communication topology-induced feasible subspace of system responses, parameterized by the communication adjacency matrix and separate from the physical plant topology. By deriving a communication link norm from this characterized subspace, we formulate a convex SLS problem for communication architecture co-design. A proximal ADMM scheme is employed to appropriately decompose the optimization problem in a manner that preserves scalability to large networks. Numerical experiments validate the efficacy of the proposed framework in optimizing the trade-off between closed-loop performance and communication architecture complexity, while also confirming its scalability.

We investigate the $k$-variation bounding property of the discrete-time Hankel operator, i.e., its invariance under the set of signals with a variation (number of sign changes) of at most $k$. Building on existing sign-consistency criteria, it is shown that this property is equivalent to the external positivity of $k+1$ explicitly realized linear discrete-time systems. Thus, making the property tractable via numerical and analytic certificates. We also derive dominant-pole restrictions for the case of $k=1$. A three-node thermal example illustrates the results and distinguishes variation bounding from variation diminishing.

Technical systems are increasingly monitored using sensor and operational data to detect degradation and performance deterioration at an early stage. However, observed trends in measurement data do not necessarily correspond to physical aging, since noise, outliers, unstable initial regions, or changing operating conditions may produce similar patterns. This paper proposes Trend-based Reference Evaluation for Degradation Detection (TREDD), an interpretable method for detecting degradation as a persistent, trend-based deviation from an early reference state. TREDD combines rolling-window smoothing, baseline estimation, a direction-dependent degradation index, long-term trend extraction, and persistent drift detection. In addition, the method separates computational drift detection from the interpretation of drift as plausible physical aging by incorporating data-quality assessment, context checking, and robust auxiliary analysis. The approach is evaluated on the NASA Lithium-Ion Battery Aging Dataset using discharge-cycle capacity as the degradation-relevant condition variable. The representative case study illustrates that TREDD can identify clear degradation trajectories while assigning reduced confidence to weak, gradual, or atypical trends. The method therefore supports transparent and confidence-based degradation interpretation rather than purely predictive battery health estimation.

Large language model (LLM) inference in AI datacenters creates a coupled control problem between GPU serving and facility cooling. Raising ambient temperature setpoints can reduce cooling energy and carbon, but also shrinks thermal headroom, induces GPU throttling, and leads to Service-Level-Objective (SLO) violations. In this paper, we study joint cooling–computing control for LLM inference: minimizing per-job GPU-plus-cooling energy while satisfying thermal safety and latency SLO constraints. We present ETCInfer, an energy-efficient, thermal-aware scheduler that selects a pre-job Computer Room Air Conditioner (CRAC) setpoint and adapts per-GPU frequency and micro-batch size during execution. ETCInfer builds compact physics-informed control models by calibrating GPU heat generation, chassis heat dissipation, CRAC power, and prefill/decode latency relations from telemetry. These models estimate hidden thermal states and time-to-throttle, enabling the scheduler to evaluate energy, temperature, and latency before applying an action. We formulate this joint setpoint–frequency–micro-batch control problem as a partially observable Markov decision process and design ETCAdapter, a learning-based controller that minimizes per-job energy under thermal safety and SLO constraints. We implement ETCInfer as a coordination layer over typical inference and cluster management stacks. Evaluation across real-trace simulation and validation experiments shows that ETCInfer reduces total job energy by up to 33.1%, thermal throttle exposure by up to 92.9%, and keeps SLO violation rates below 0.7% even at ambient temperatures up to $48^{\circ}\mathrm{C}$.

This letter proposes a formal synthesis of Robust Koopman-Model Predictive Control (RK-MPC), a novel data-driven approach to formal synthesis of systems with nonlinear dynamics. We formulate a novel optimization problem for RK-MPC by incorporating specifications described by Signal Temporal Logic and prove its closed-loop performance. Effectiveness of the proposed RK-MPC is evaluated by applying it to the reliable design of an AC-DC power converter.

Practical reconfigurable intelligent surfaces (RISs) can exhibit deterministic parasitic scattering that is not captured by idealized element-wise models. As a result, such models may overestimate the gain of the intended RIS-assisted path and bias multipath prediction. This paper develops a full-wave-calibrated element-wise model using three Bragg-order basis functions to represent the intended and dominant parasitic scattering components. Environmental multipath is incorporated by identifying RIS–Rx reflection sequences with ray tracing (RT) and unfolding them by image theory into path-dependent image points. This allows direct and reflected RIS-assisted paths to be evaluated using the same per-element kernel and coherently combined with Tx–Rx bypass paths. To reduce the full-wave calibration burden for large RISs under a prescribed focusing configuration, only the three Bragg-order coefficients are transferred from a 25-by-25 calibration aperture, while the target-aperture basis functions and geometry are recomputed. At 154 GHz, the transferred coefficients keep the intended-order errors within 0.8 dB for the 50-by-50 and 100-by-100 RISs, while reducing the full-wave calibration time for the prescribed configuration from 7.56 to 1.08 h relative to direct 100-by-100 calibration. For multipath validation with the 50-by-50 RIS, the calibrated model using the same transferred coefficients keeps the nominal-Rx gain error within 0.88 dB across four PEC reflector configurations, compared with 1.75–5.51 dB for the uncalibrated general model, and reduces the error from 6.49 to 0.23 dB in the scaled indoor environment.

A finite-difference bowed-string model with implicitly resolved Stribeck friction is presented, with a regime diagnostic. Without implicit resolution no stick phase forms at any bow force. With friction, impedance and quality factor from published measurement rather than fitted, all four strings return a stick fraction of 89.1% against an ideal 90%. Schelleng's maximum bow force is recovered on every string. His minimum, $F_{\min} \propto Z^2 v_b \beta^{-2}$, is replaced by a law, $F_{\min} = C Z v_b / \beta$ with a dimensionless $C = 1.112 \pm 0.017$ that reproduces on sixty-four held-out operating points. Six controllers at matched capacity, on four strings at twenty seeds, place a gated recurrent network ahead of a feedforward one, its margin over the mean of the other five largest when the commanded bow speed is overridden at mid-stroke. The feedforward network completes more strokes, mostly from a cold start no player would use. A minimal gated variant fails because gates computed from the input alone cannot clear a latched state. Training loss selects neither the capacity nor the context length. No learned controller improves on the lookup rule that generated its labels where that rule is correct. Of the rule's 1089 cells, 57 are playable on a properly settled plant and unplayable by its labels, and the controller commands them where the lookup will not. The controller's score regresses on the rule's with a slope of 0.32, more than ten standard errors below unity, so it overtakes the rule where the rule fails and is bounded by it where it holds. Under a rigid finger stop the plant is provably invariant, so transfer loss between pitches is the controller's alone, traced to one feature. A regime classifier without a stick test mistakes small-amplitude periodic slipping for Helmholtz motion, and a harmonicity measure rates a string the bow never grips above it.

Congestion pricing is widely used to improve transportation network efficiency where individual decentralized route choices can lead to system-level inefficiencies. Pigouvian tolls achieve the system optimum but depend on demand and can lose effectiveness under demand shifts, particularly on structurally critical bottleneck edges. We propose a topology-aware regularization of the Beckmann potential governing the (Wardrop) equilibrium by augmenting precomputed Pigouvian tolls with an offline structural penalty derived from Forman–Ricci curvature (FRC), which captures bottleneck structures. We prove that the resulting equilibrium is well-defined, that stronger regularization reduces flow on penalized edges, and that efficiency loss depends on how closely the structural penalty approximates optimal Pigouvian tolls. Experiments on eight real-world networks under targeted and uniform demand perturbations show reduced bottleneck flow with near-optimal efficiency. FRC achieves reductions comparable to edge betweenness at substantially lower computational cost, making it a practical, robust complement to classical congestion pricing.

We study decentralized learning of Nash equilibria (NE) in infinite-horizon discounted Markov games under bandit feedback, focusing on Markov $\alpha$-potential games. We develop KL-projected natural policy gradient (NPG) algorithms in two settings: an episodic setting with frozen policies during sampling and a fully online setting in which players receive a single realized cost sample per time step and update their policies asynchronously along a continuing trajectory. We establish finite-time high-probability NE regret bounds of order $\widetilde O(T^{-1/4})$ and $\widetilde O(T^{-2/15})$ for the episodic and fully online settings, respectively, up to fixed approximation terms. Crucially, our bounds eliminate the distribution-mismatch coefficient, which can scale prohibitively with the size of the state space, while accommodating potential approximation, estimation-oracle bias, and transition sensitivity. We further identify a state-wise potential structure that yields sharper guarantees with additive dependence on the potential approximation error $\alpha$. We specialize the framework to independent-resource Markov congestion games (IMCGs), establish their approximate-potential and transition-sensitivity properties, and construct decentralized estimation oracles from realized costs. As an application, we introduce strategic online job scheduling on stochastic machines and obtain a scalable decentralized algorithm for learning stable dispatching policies. Overall, our results provide the first finite-time high-probability NE regret guarantees for fully online asynchronous decentralized learning in Markov $\alpha$-potential games, remove distribution-mismatch coefficients from the regret bounds, accommodate fixed estimation-oracle bias, and provide scalable decentralized learning with finite-time guarantees for IMCGs.

We consider boundary stabilization of a heavy chain hanging from a moving trolley with no tip load. Because the tension vanishes at the free end, the wave speed vanishes there; in Riemann coordinates the model becomes a degenerate $2\times2$ hyperbolic system in which the coupling is singular and the free-end reflection is generated in the domain rather than by a boundary condition. We construct a Volterra backstepping transformation that maps this system to the same chain with uniform damping of an arbitrarily prescribed rate and an elastic restraint at the trolley, yielding exponential convergence of displacement, velocity, and strain to zero. The singular kernel equations are solved by selecting their bounded Frobenius branch at the free end, which replaces the missing boundary datum, and the four kernels are generated by a globally convergent power series. The transformation is boundedly invertible on the energy space, with inverse obtained by reversing the prescribed decay rate. The result extends the radial backstepping structure developed for parabolic equations on disks and balls to a degenerate hyperbolic system.