pwc-archive/papers-with-abstracts huggingface dataset:
Smooth Logic Constraints in Nonlinear Optimization and Optimal Control Problems
Authors: J. Wehbeh, E. C. Kerrigan
Date: 2025-06-02 00:00:00

In some optimal control problems, complex relationships between states and inputs cannot be easily represented using continuous constraints, necessitating the use of discrete logic instead. This paper presents a method for incorporating such logic constraints directly within continuous optimization frameworks, eliminating the need for binary variables or specialized solvers. Our approach reformulates arbitrary logic constraints under minimal assumptions as max-min constraints, which are then smoothed by introducing auxiliary variables into the optimization problem. When these reformulated constraints are satisfied, they guarantee that the original logical conditions hold, ensuring correctness in the optimization process. We demonstrate the effectiveness of this method on two planar quadrotor control tasks with complex logic constraints. Compared to existing techniques for encoding logic in continuous optimization, our approach achieves faster computational performance and improved convergence to feasible solutions.
Update-Aware Robust Optimal Model Predictive Control for Nonlinear Systems
Authors: J. Wehbeh, E. C. Kerrigan
Date: 2025-06-02 00:00:00
Tasks: Model Predictive Control

Robust optimal or min-max model predictive control (MPC) approaches aim to guarantee constraint satisfaction over a known, bounded uncertainty set while minimizing a worst-case performance bound. Traditionally, these methods compute a trajectory that meets the desired properties over a fixed prediction horizon, apply a portion of the resulting input, and then re-solve the MPC problem using newly obtained measurements at the next time step. However, this approach fails to account for the fact that the control trajectory will be updated in the future, potentially leading to conservative designs. In this paper, we present a novel update-aware robust optimal MPC algorithm for decreasing horizon problems on nonlinear systems that explicitly accounts for future control trajectory updates. This additional insight allows our method to provably expand the feasible solution set and guarantee improved worst-case performance bounds compared to existing techniques. Our approach formulates the trajectory generation problem as a sequence of nested existence-constrained semi-infinite programs (SIPs), which can be efficiently solved using local reduction techniques. To demonstrate its effectiveness, we evaluate our approach on a planar quadrotor problem, where it clearly outperforms an equivalent method that does not account for future updates at the cost of increased computation time.
A Vertical Approach to Designing and Managing Sustainable Heterogeneous Edge Data Centers
Authors: Aikaterini Maria Panteleaki, Varatheepan Paramanayakam, Vasileios Pentsos, Andreas Karatzas, Spyros Tragoudas, Iraklis Anagnostopoulos
Date: 2025-06-02 00:00:00
Tasks: Scheduling

The increasing demand for Artificial Intelligence (AI) computing poses significant environmental challenges, with both operational and embodied carbon emissions becoming major contributors. This paper presents a carbon-aware holistic methodology for designing and managing sustainable Edge Data Centers (EDCs), based on three design principles that challenge the state-of-the-art optimization paradigms. Our approach employs vertical integration across the architecture, system, and runtime layers, balances operational and embodied carbon emissions while considering EDC performance as a co-optimization objective, rather than a constraint. At the architecture level, we propose carbon-aware and approximate accelerator designs to reduce embodied carbon. At the system level, we enhance resource utilization and adapt to real-time carbon intensity variations to minimize operational emissions. Finally, at the runtime level, we develop dynamic scheduling frameworks that adjust execution, based on energy constraints and carbon intensity.
Interpretable reinforcement learning for heat pump control through asymmetric differentiable decision trees
Authors: Toon Van Puyvelde, Mehran Zareh, Chris Develder
Date: 2025-06-02 00:00:00
Tasks: Decision Making, Deep Reinforcement Learning, energy management, Management, reinforcement-learning, Reinforcement Learning

In recent years, deep reinforcement learning (DRL) algorithms have gained traction in home energy management systems. However, their adoption by energy management companies remains limited due to the black-box nature of DRL, which fails to provide transparent decision-making feedback. To address this, explainable reinforcement learning (XRL) techniques have emerged, aiming to make DRL decisions more transparent. Among these, soft differential decision tree (DDT) distillation provides a promising approach due to the clear decision rules they are based on, which can be efficiently computed. However, achieving high performance often requires deep, and completely full, trees, which reduces interpretability. To overcome this, we propose a novel asymmetric soft DDT construction method. Unlike traditional soft DDTs, our approach adaptively constructs trees by expanding nodes only when necessary. This improves the efficient use of decision nodes, which require a predetermined depth to construct full symmetric trees, enhancing both interpretability and performance. We demonstrate the potential of asymmetric DDTs to provide transparent, efficient, and high-performing decision-making in home energy management systems.
Equivalence of Left- and Right-Invariant Extended Kalman Filters on Matrix Lie Groups
Authors: Finn G. Maurer, Erlend A. Basso, Henrik M. Schmidt-Didlaukies, Torleiv H. Bryne
Date: 2025-06-02 00:00:00

This paper derives the extended Kalman filter (EKF) for continuous-time systems on matrix Lie groups observed through discrete-time measurements. By modeling the system noise on the Lie algebra and adopting a Stratonovich interpretation for the stochastic differential equation (SDE), we ensure that solutions remain on the manifold. The derivation of the filter follows classical EKF principles, naturally integrating a necessary full-order covariance reset post-measurement update. A key contribution is proving that this full-order covariance reset guarantees that the Lie-group-valued state estimate is invariant to whether a left- or right-invariant error definition is used in the EKF. Monte Carlo simulations of the aided inertial navigation problem validate the invariance property and confirm its absence when employing reduced-order covariance resets.
Optimal Co-Design of a Hybrid Energy Storage System for Truck Charging
Authors: Juan Pablo Bertucci, Sudarshan Raghuraman, Mauro Salazar, Theo Hofman
Date: 2025-06-02 00:00:00

The major challenges to battery electric truck adoption are their high cost and grid congestion.In this context, stationary energy storage systems can help mitigate both issues. Since their design and operation are strongly coupled, to make the best out of them, they should be jointly optimized. This paper presents a co-design framework for hybrid energy storage systems where their technology and sizing are optimized jointly with their operational strategies. Specifically, we consider a microgrid supporting truck chargers that consists of utility grid, solar panels, and energy storage systems including batteries, supercapacitors and flywheels. We frame the co-design problem as a mixed-integer linear program that can be solved with global optimality guarantees. We showcase our framework in a case-study of a distribution center in the Netherlands. Our results show that although the battery-only configuration is already competitive, adding supercapacitors or flywheel storage decrease total cost and increase energy sold back to the grid. Overall, the fully hybrid solution (Battery+Supercapacitors+Flywheel) offers the best outcomes, achieving the lowest overall cost (1.96\% lower compared to battery-only) and reduced grid dependency, but at a higher (2.6\%) initial investment.
Prediction of the Conditional Probability Densities of Time Interval Extrema with Application to Risk-Sensitive Scheduling
Authors: Buyi Yu, Wenyuan Tang
Date: 2025-06-02 00:00:00
Tasks: Scheduling

Planning and scheduling activities in the electrical power system, such as the commitment of reserve generation, often involve the statistical characterization of peak demand. Due to the stationarity assumption of classical extreme value analysis (EVA), existing approaches in the industry apply EVA on simulated annual peaks created by weather-dependent surrogate models using Monte-Carlo simulations on a per-scenario basis. In day-ahead scheduling, the daily peak demand changes upon various factors besides temperature, Monte-Carlo experiments become intractable, and state-of-the-art generalized additive model for location, scale and shape (GAMLSS)-based nonstationary EVA is often impractical due to convergence issues on high-dimensional covariates. This article explores uncharted territories and proposes a novel nonstationary EVA estimator that predicts the probable peaks of high-resolution time intervals and their corresponding conditional probability densities based on calendar information and weather conditions where historical peaks are observed. Compared to GAMLSS, our method automatically discovers and robustly models complex relationships between the covariate and the peak demand density. We present a case study on the determination of day-ahead scheduling capacity and demonstrate that compared to the industry approach, our approach results in a 38% reduction in the yearly total committed capacity while maintaining the given risk requirement.
Distributed perception of social power in influence networks with stubborn individuals
Authors: Ye Tian, Yu Kawano, Wei zhang, Kenji Kashima
Date: 2025-06-01 00:00:00

Social power quantifies the ability of individuals to influence others and plays a central role in social influence networks. Yet computing social power typically requires global knowledge and significant computational or storage capability, especially in large-scale networks with stubborn individuals. This paper develops distributed algorithms for social power perception in groups with stubborn individuals. We propose two dynamical models for distributed perception of social power based on the Friedkin-Johnsen (FJ) opinion dynamics: one without and one with reflected appraisals. In both scenarios, our perception mechanism begins with independent initial perceptions and relies primarily on local information: each individual only needs to know its neighbors' stubbornness or self-appraisals, the influence weights they accord and the group size. We provide rigorous dynamical system analysis to characterize the properties of equilibria, invariant sets and convergence. Conditions under which individuals' perceived social power converges to the actual social power are established. The proposed perception mechanism demonstrates strong robustness to reflected appraisals, irrational perceptions, and timescale variations. Numerical examples are provided to illustrate our results.
Generative diffusion posterior sampling for informative likelihoods
Authors: Zheng Zhao
Date: 2025-06-01 00:00:00

Sequential Monte Carlo (SMC) methods have recently shown successful results for conditional sampling of generative diffusion models. In this paper we propose a new diffusion posterior SMC sampler achieving improved statistical efficiencies, particularly under outlier conditions or highly informative likelihoods. The key idea is to construct an observation path that correlates with the diffusion model and to design the sampler to leverage this correlation for more efficient sampling. Empirical results conclude the efficiency.
Chaotic Noncoherent SWIPT in Multi-Functional RIS-Aided Systems
Authors: Priyadarshi Mukherjee, Constantinos Psomas, Himal A. Suraweera, Ioannis Krikidis
Date: 2025-06-01 00:00:00

In this letter, we investigate the design of chaotic signal-based transmit waveforms in a multi-functional reconfigurable intelligent surface (MF-RIS)-aided set-up for simultaneous wireless information and power transfer. We propose a differential chaos shift keying-based MF-RIS-aided set-up, where the MF-RIS is partitioned into three non-overlapping surfaces. The elements of the first sub-surface perform energy harvesting (EH), which in turn, provide the required power to the other two sub-surfaces responsible for transmission and reflection of the incident signal. By considering a frequency selective scenario and a realistic EH model, we characterize the chaotic MF-RIS-aided system in terms of its EH performance and the associated bit error rate. Thereafter, we characterize the harvested energy-bit error rate trade-off and derive a lower bound on the number of elements required to operate in the EH mode. Accordingly, we propose novel transmit waveform designs to demonstrate the importance of the choice of appropriate system parameters in the context of achieving self-sustainability.
IAE Optimized PID Tuning with Phase Margin and Crossover Frequency Constraints
Authors: Senol Gulgonul
Date: 2025-06-01 00:00:00

This paper presents PMwc-Tune, a novel PID tuning method that uniquely combines frequency-domain robustness constraints with time-domain performance optimization through constrained nonlinear programming. The key contribution is a unified formulation that simultaneously enforces phase margin and crossover frequency requirements (via nonlinear equality constraints) while minimizing the Integral Absolute Error (IAE) of the closed-loop response. The algorithm employs Sequential Quadratic Programming (SQP) to solve this constrained optimization problem, guaranteeing specification attainment within numerical tolerances while optimizing transient performance. Numerical validation on benchmark systems demonstrates precise convergence to design targets (phase margin and crossover frequency errors <1%) with a 4.6% IAE reduction compared to MATLAB's pidtune. The open-source implementation provides both methodological transparency and practical design flexibility, enabling PID controllers that rigorously balance frequency-domain robustness and time-domain performance.
HMPC-assisted Adversarial Inverse Reinforcement Learning for Smart Home Energy Management
Authors: Jiadong He, Liang Yu, Zhiqiang Chen, Dawei Qiu, Dong Yue, Goran Strbac, Meng Zhang, Yujian Ye, Yi Wang
Date: 2025-06-01 00:00:00
Tasks: energy management, Management, Model Predictive Control, reinforcement-learning, Reinforcement Learning

This letter proposes an Adversarial Inverse Reinforcement Learning (AIRL)-based energy management method for a smart home, which incorporates an implicit thermal dynamics model. In the proposed method, historical optimal decisions are first generated using a neural network-assisted Hierarchical Model Predictive Control (HMPC) framework. These decisions are then used as expert demonstrations in the AIRL module, which aims to train a discriminator to distinguish expert demonstrations from transitions generated by a reinforcement learning agent policy, while simultaneously updating the agent policy that can produce transitions to confuse the discriminator. The proposed HMPC-AIRL method eliminates the need for explicit thermal dynamics models, prior or predictive knowledge of uncertain parameters, or manually designed reward functions. Simulation results based on real-world traces demonstrate the effectiveness and data efficiency of the proposed method.
Interpretable Spatio-Temporal Features Extraction based Industrial Process Modeling and Monitoring by Soft Sensor
Authors: Qianchao Wang, Peng Sha, Leena Heistrene, Yuxuan Ding, Yaping Du
Date: 2025-06-01 00:00:00

Data-driven soft sensors have been widely applied in complex industrial processes. However, the interpretable spatio-temporal features extraction by soft sensors remains a challenge. In this light, this work introduces a novel method termed spatio-temporal consistent and interpretable model (STCIM). First, temporal and spatial features are captured and aligned by a far topological spatio-temporal consistency extraction block. Then, the features are mapped into an interpretable latent space for further prediction by explicitly giving physical meanings to latent variables. The efficacy of the proposed STCIM is demonstrated through the modeling of two generated datasets and a real-life dataset of coal-fired power plants. The corresponding experiments show: 1) The generalization of STCIM outperforms other methods, especially in different operation situations. 2) The far topological spatio-temporal consistency is vital for feature alignment. 3) The hyper-parameters of physics-informed interpretable latent space loss decide the performance of STCIM.
Conceal Truth while Show Fake: T/F Frequency Multiplexing based Anti-Intercepting Transmission
Authors: Zhisheng Yin, Nan Cheng, Mingjie Wang, Changle Li, Wei Xiang
Date: 2025-06-01 00:00:00

In wireless communication adversarial scenarios, signals are easily intercepted by non-cooperative parties, exposing the transmission of confidential information. This paper proposes a true-and-false (T/F) frequency multiplexing based anti-intercepting transmission scheme capable of concealing truth while showing fake (CTSF), integrating both offensive and defensive strategies. Specifically, through multi-source cooperation, true and false signals are transmitted over multiple frequency bands using non-orthogonal frequency division multiplexing. The decoy signals are used to deceive non-cooperative eavesdropper, while the true signals are hidden to counter interception threats. Definitions for the interception and deception probabilities are provided, and the mechanism of CTSF is discussed. To improve the secrecy performance of true signals while ensuring decoy signals achieve their deceptive purpose, we model the problem as maximizing the sum secrecy rate of true signals, with constraint on the decoy effect. Furthermore, we propose a bi-stage alternating dual-domain optimization approach for joint optimization of both power allocation and correlation coefficients among multiple sources, and a Newton's method is proposed for fitting the T/F frequency multiplexing factor. In addition, simulation results verify the efficiency of anti-intercepting performance of our proposed CTSF scheme.
Action Dependency Graphs for Globally Optimal Coordinated Reinforcement Learning
Authors: Jianglin Ding, Jingcheng Tang, Gangshan Jing
Date: 2025-06-01 00:00:00
Tasks: Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning

Action-dependent individual policies, which incorporate both environmental states and the actions of other agents in decision-making, have emerged as a promising paradigm for achieving global optimality in multi-agent reinforcement learning (MARL). However, the existing literature often adopts auto-regressive action-dependent policies, where each agent's policy depends on the actions of all preceding agents. This formulation incurs substantial computational complexity as the number of agents increases, thereby limiting scalability. In this work, we consider a more generalized class of action-dependent policies, which do not necessarily follow the auto-regressive form. We propose to use the `action dependency graph (ADG)' to model the inter-agent action dependencies. Within the context of MARL problems structured by coordination graphs, we prove that an action-dependent policy with a sparse ADG can achieve global optimality, provided the ADG satisfies specific conditions specified by the coordination graph. Building on this theoretical foundation, we develop a tabular policy iteration algorithm with guaranteed global optimality. Furthermore, we integrate our framework into several SOTA algorithms and conduct experiments in complex environments. The empirical results affirm the robustness and applicability of our approach in more general scenarios, underscoring its potential for broader MARL challenges.
Bi-Level optimization for parameter estimation of differential equations using interpolation
Authors: Siddharth Prabhu, Srinivas Rangarajan, Mayuresh Kothare
Date: 2025-05-31 00:00:00
Tasks: Model Discovery, parameter estimation

Inverse problem or parameter estimation of ordinary differential equations is a process of obtaining the best parameters using experimental measurements of the states. Single (Multiple)-shooting is a type of sequential optimization method that minimizes the error in the measured and numerically integrated states. However, this requires computing sensitivities i.e. the derivatives of states with respect to the parameters over the numerical integrator, which can get computationally expensive. To address this challenge, many interpolation-based approaches have been proposed to either reduce the computational cost of sensitivity calculations or eliminate their need. In this paper, we use a bi-level optimization framework that leverages interpolation and exploits the structure of the differential equation to solve an inner convex optimization problem. We apply this method to two different problem formulations. First, parameter estimation for differential equations, and delayed differential equations, where the model structure is known but the parameters are unknown. Second, model discovery problems, where both the model structure and parameters are unknown.
Integrative, Scalable Modeling of Hydrological Systems with MBSE and HFGT
Authors: Megan Harris, Ehsanoddin Ghorbanichemazkati, Mohammad Mahdi Naderi, John C. Little, Amro M. Farid
Date: 2025-05-31 00:00:00

Worsening global challenges in the Anthropocene demand complex, adaptive solutions grounded in a systems-level understanding of coupled social and environmental dynamics. However, existing modeling approaches often fall short due to disciplinary silos, limited scalability, and the absence of shared ontological frameworks. Model-Based Systems Engineering (MBSE), when integrated with Hetero-functional Graph Theory (HFGT), offers a powerful methodology for modeling systems of systems while preserving subsystem heterogeneity and enabling cross-disciplinary integration. This paper presents the first application of the MBSE-HFGT methodology to environmental systems, using a series of worked examples involving flow through lake and land segments. These examples demonstrate how the approach enables consistent, scalable, and integrative modeling of complex environmental processes.
Sensor Fusion Methods for Gaussian Mixture Models
Authors: Ishan Paranjape, Islam Hussein, Jeremy Murray-Krezan, Sean Phillips, Suman Chakravorty
Date: 2025-05-31 00:00:00
Tasks: Sensor Fusion, State Estimation

Consensus is a popular technique for distributed state estimation. This formulation allows networks of connected agents or sensors to exchange information about the distribution of a set of targets with their immediate neighbors without the need of a centralized node or layer. We present decentralized consensus-based fusion techniques for a system whose target prior estimates are a weighted mixture of Gaussian probability density functions (PDFs) for the following cases: 1) in which all agents have the same a priori Gaussian mixture estimate of the target, and 2) in which agents have different a priori Gaussian mixture estimates of the target. For the second case, we present a formulation that fuses each agent's a priori estimate without using local observations such that each agent's posterior estimate is the same across the network.