pwc-archive/papers-with-abstracts huggingface dataset:
Synthetic Time Series Forecasting with Transformer Architectures: Extensive Simulation Benchmarks
Authors: Ali Forootani, Mohammad Khosravi
Date: 2025-05-26 00:00:00
Tasks: Benchmarking, Decision Making Under Uncertainty, Inductive Bias, Time Series, Time Series Forecasting

Time series forecasting plays a critical role in domains such as energy, finance, and healthcare, where accurate predictions inform decision-making under uncertainty. Although Transformer-based models have demonstrated success in sequential modeling, their adoption for time series remains limited by challenges such as noise sensitivity, long-range dependencies, and a lack of inductive bias for temporal structure. In this work, we present a unified and principled framework for benchmarking three prominent Transformer forecasting architectures-Autoformer, Informer, and Patchtst-each evaluated through three architectural variants: Minimal, Standard, and Full, representing increasing levels of complexity and modeling capacity. We conduct over 1500 controlled experiments on a suite of ten synthetic signals, spanning five patch lengths and five forecast horizons under both clean and noisy conditions. Our analysis reveals consistent patterns across model families. To advance this landscape further, we introduce the Koopman-enhanced Transformer framework, Deep Koopformer, which integrates operator-theoretic latent state modeling to improve stability and interpretability. We demonstrate its efficacy on nonlinear and chaotic dynamical systems. Our results highlight Koopman based Transformer as a promising hybrid approach for robust, interpretable, and theoretically grounded time series forecasting in noisy and complex real-world conditions.
Efficient Gaussian Mixture Filters based on Transition Density Approximation
Authors: Ondŕej Straka, Uwe D. Hanebeck
Date: 2025-05-26 00:00:00

Gaussian mixture filters for nonlinear systems usually rely on severe approximations when calculating mixtures in the prediction and filtering step. Thus, offline approximations of noise densities by Gaussian mixture densities to reduce the approximation error have been proposed. This results in exponential growth in the number of components, requiring ongoing component reduction, which is computationally complex. In this paper, the key idea is to approximate the true transition density by an axis-aligned Gaussian mixture, where two different approaches are derived. These approximations automatically ensure a constant number of components in the posterior densities without the need for explicit reduction. In addition, they allow a trade-off between estimation quality and computational complexity.
Interpretable Augmented Physics-Based Model for Estimation and Tracking
Authors: Ondřej Straka, Jindřich Duník, Pau Closas, Tales Imbiriba
Date: 2025-05-26 00:00:00

State-space estimation and tracking rely on accurate dynamical models to perform well. However, obtaining an vaccurate dynamical model for complex scenarios or adapting to changes in the system poses challenges to the estimation process. Recently, augmented physics-based models (APBMs) appear as an appealing strategy to cope with these challenges where the composition of a small and adaptive neural network with known physics-based models (PBM) is learned on the fly following an augmented state-space estimation approach. A major issue when introducing data-driven components in such a scenario is the danger of compromising the meaning (or interpretability) of estimated states. In this work, we propose a novel constrained estimation strategy that constrains the APBM dynamics close to the PBM. The novel state-space constrained approach leads to more flexible ways to impose constraints than the traditional APBM approach. Our experiments with a radar-tracking scenario demonstrate different aspects of the proposed approach and the trade-offs inherent in the imposed constraints.
Dynamically Learned Test-Time Model Routing in Language Model Zoos with Service Level Guarantees
Authors: Herbert Woisetschläger, Ryan Zhang, Shiqiang Wang, Hans-Arno Jacobsen
Date: 2025-05-26 00:00:00
Tasks: Language Modeling, Language Modelling, model, Model Selection, Stochastic Optimization

Open-weight LLM zoos provide access to numerous high-quality models, but selecting the appropriate model for specific tasks remains challenging and requires technical expertise. Most users simply want factually correct, safe, and satisfying responses without concerning themselves with model technicalities, while inference service providers prioritize minimizing operating costs. These competing interests are typically mediated through service level agreements (SLAs) that guarantee minimum service quality. We introduce MESS+, a stochastic optimization algorithm for cost-optimal LLM request routing while providing rigorous SLA compliance guarantees. MESS+ learns request satisfaction probabilities of LLMs in real-time as users interact with the system, based on which model selection decisions are made by solving a per-request optimization problem. Our algorithm includes a novel combination of virtual queues and request satisfaction prediction, along with a theoretical analysis of cost optimality and constraint satisfaction. Across a wide range of state-of-the-art LLM benchmarks, MESS+ achieves an average of 2x cost savings compared to existing LLM routing techniques.
Persistently Exciting Online Feedback Optimization Controller with Minimal Perturbations
Authors: Tore Gude, Marta Anna Zagorowska, Lars Struen Imsland
Date: 2025-05-26 00:00:00
Tasks: Bilevel Optimization

This paper develops a persistently exciting input generating Online Feedback Optimization (OFO) controller that estimates the sensitivity of a process ensuring minimal deviations from the descent direction while converging. This eliminates the need for random perturbations in feedback loop. The proposed controller is formulated as a bilevel optimization program, where a nonconvex full rank constraint is relaxed using linear constraints and penalization. The validation of the method is performed in a simulated scenario where multiple systems share a limited, costly resource for production optimization, simulating an oil and gas resource allocation problem. The method allows for less input perturbations while accurately estimating gradients, allowing faster convergence when the gradients are unknown. In the case study, the proposed method achieved the same profit compared to an OFO controller with random input perturbations, and $1.4\%$ higher profit compared to an OFO controller without input perturbations.
Optimizing Offshore Wind Integration through Multi-Terminal DC Grids: A Market-Based OPF Framework for the North Sea Interconnectors
Authors: Bernardo Castro Valerio, Vinícius Albernaz Lacerda, Marc Cheah-Mañe, Pieter Gebraad, Oriol Gomis-Bellmunt
Date: 2025-05-26 00:00:00

Interconnecting price zones and remote renewable energy sources has emerged as a key solution to achieving climate goals. The objective of this work is to present a formulation that extends the base optimal power flow model with price zones constraints to forecast the operations of upcoming offshore wind developments integrated into a multi-terminal DC grid. A case study based on the 2030 development of the North Sea is used to exemplify the utilization of the formulation. Here, three cases are presented, one with the price as a parameter and the other two with the price as a variable dependent on power flows between price zones. The paper demonstrates that, for large power flows, it is necessary to include additional constraints beyond line limitations to accurately capture the effects of price zone exchanges.
A fully automated urban PV parameterization framework for improved estimation of energy production profiles
Authors: Bowen Tian, Roel C. G. M. Loonen, Roland Valckenborg, Jan L. M. Hensen
Date: 2025-05-26 00:00:00
Tasks: Semantic Segmentation

Accurate parameterization of rooftop photovoltaic (PV) installations is critical for effective grid management and strategic large-scale solar deployment. The lack of high-fidelity datasets for PV configuration parameters often compels practitioners to rely on coarse assumptions, undermining both the temporal and numerical accuracy of large-scale PV performance modeling. This study introduces a fully automated framework that innovatively integrates remote sensing data, semantic segmentation, polygon-vector refinement, tilt-azimuth estimation, and module layout inference to produce a richly attributed GIS dataset of distributed PV. Applied to Eindhoven (the Netherlands), the method achieves a correlation ($R^2$) of 0.92 with Distribution System Operator (DSO) records, while capacity estimates for 73$\%$ of neighborhoods demonstrate agreement within a $\pm$25$\%$ margin of recorded data. Additionally, by accurately capturing actual system configuration parameters (e.g., tilt, azimuth, module layout) and seamlessly linking them to advanced performance models, the method yields more reliable PV energy generation forecasts within the distribution networks. Centering our experiments toward a high PV-penetration community, configuration-aware simulations help to reduce Mean Absolute Percentage Error (MAPE) of energy generation modeling by up to 160$\%$ compared to the conventional assumption-based approaches. Furthermore, owing to its modular design and reliance on readily available geospatial resources, the workflow can be extended across diverse regions, offering a scalable solution for robust urban solar integration.
Chance-constrained Solar PV Hosting Capacity Assessment for Distribution Grids Using Gaussian Process and Logit Learning
Authors: Sel Ly, Anshuman Singh, Petr Vorobev, Yeng Chai Soh, Hung Dinh Nguyen
Date: 2025-05-26 00:00:00
Tasks: Management

Growing penetration of distributed generation such as solar PV can increase the risk of over-voltage in distribution grids, affecting network security. Therefore, assessment of the so-called, PV hosting capacity (HC) - the maximum amount of PV that a given grid can accommodate becomes an important practical problem. In this paper, we propose a novel chance-constrained HC estimation framework using Gaussian Process and Logit learning that can account for uncertainty and risk management. Also, we consider the assessment of HC under different voltage control strategies. Our results have demonstrated that the proposed models can achieve high accuracy levels of up to 93% in predicting nodal over-voltage events on IEEE 33-bus and 123-bus test-cases. Thus, these models can be effectively employed to estimate the chance-constrained HC with various risk levels. Moreover, our proposed methods have simple forms and low computational costs of only a few seconds.
Scalable quantile predictions of peak loads for non-residential customer segments
Authors: Shaohong Shi, Jacco Heres, Simon H. Tindemans
Date: 2025-05-26 00:00:00
Tasks: quantile regression

Electrical grid congestion has emerged as an immense challenge in Europe, making the forecasting of load and its associated metrics increasingly crucial. Among these metrics, peak load is fundamental. Non-time-resolved models of peak load have their advantages of being simple and compact, and among them Velander's formula (VF) is widely used in distribution network planning. However, several aspects of VF remain inadequately addressed, including year-ahead prediction, scaling of customers, aggregation, and, most importantly, the lack of probabilistic elements. The present paper proposes a quantile interpretation of VF that enables VF to learn truncated cumulative distribution functions of peak loads with multiple quantile regression under non-crossing constraints. The evaluations on non-residential customer data confirmed its ability to predict peak load year ahead, to fit customers with a wide range of electricity consumptions, and to model aggregations of customers. A noteworthy finding is that for a given electricity consumption, aggregations of customers have statistically larger peak loads than a single customer.
IAE Optimized PID Tuning via Second Order Step Response Target Matching
Authors: Senol Gulgonul
Date: 2025-05-22 00:00:00

This paper presents SOSTIAE (Second-Order System Target IAE), a novel PID tuning method that combines IAE minimization with explicit transient response shaping for practical control applications. The algorithm generates optimal PID parameters by matching the closed-loop response to a target second-order system with user-defined settling time (Ts) and percent overshoot (PO), while maintaining the conventional IAE performance metric. Comparative evaluations on first to third-order systems demonstrate that SOSTIAE consistently outperforms MATLAB's proprietary pidtune function, achieving 47-67% lower overshoot and up to 26% better IAE performance for higher-order plants. The constrained optimization framework ensures physically realizable controllers by enforcing non-negative PID gains and stability criteria, addressing known limitations of unconstrained IAE methods. Results indicate that SOSTIAE provides engineers with a systematic alternative for PID tuning when transient specifications and practical implementation constraints are critical.
Range Space or Null Space: Least-Squares Methods for the Realization Problem
Authors: Jiabao He, Yueyue Xu, Yue Ju, Cristian R. Rojas, Håkan Hjalmarsson
Date: 2025-05-26 00:00:00

This contribution revisits the classical approximate realization problem, which involves determining matrices of a state-space model based on estimates of a truncated series of Markov parameters. A Hankel matrix built up by these Markov parameters plays a fundamental role in this problem, leveraging the fact that both its range space and left null space encode critical information about the state-space model. We examine two prototype realization algorithms based on the Hankel matrix: the classical range-space-based (SVD-based) method and the more recent null-space-based method. It is demonstrated that the range-space-based method corresponds to a total least-squares solution, whereas the null-space-based method corresponds to an ordinary least-squares solution. By analyzing the differences in sensitivity of the two algorithms, we determine the conditions when one or the other realization algorithm is to be preferred, and identify factors that contribute to an ill-conditioned realization problem. Furthermore, recognizing that both methods are suboptimal, we argue that the optimal realization is obtained through a weighted least-squares approach. A statistical analysis of these methods, including their consistency and asymptotic normality is also provided.
Customising Electricity Contracts at Scale with Large Language Models
Authors: Jochen L. Cremer
Date: 2025-05-26 00:00:00

The electricity system becomes more complex, connecting massive numbers of end-users and distributed generators. Adding or removing grid connections requires expert studies to align technical constraints with user requests. In times of labour shortages, carrying out these studies represents a significant amount of time that engineers at system operators spend in planning departments. As time is limited, only standard block connectivity contracts can be offered to end-users, or the requests pile up. Even if offers are made, these often do not perfectly match the user's requirements, leading to overpaying or underusing the grid capacity. This paper investigates whether end-users can negotiate individual, flexible time-of-use contracts directly with the grid using Large Language Models (LLM) in chats at scale. The LLM-based chat has direct access to a model of the grid and studies the grid's technical constraints just as an expert engineer. The advantage of this system is that end-users can directly interact with grid models through natural language; no intermediate is needed to service, analyse, study, assess, advise, consult and engineer. This initial study paves the way toward developing this tailored LLM system, resulting in possible high-efficiency gains for grid planning and customer management.
Synchronous Models and Fundamental Systems in Observer Design
Authors: Pieter van Goor, Robert Mahony
Date: 2025-05-26 00:00:00

This paper introduces the concept of a synchronous model as an extension of the internal model concept used in observer design for dynamical systems. A system is said to contain a synchronous model of another if there is a suitable error function between the two systems that remains stationary for all of the trajectories of the two systems. A system is said to admit a synchronous lift if a second system containing a synchronous model exists. We provide necessary and sufficient conditions that a system admits a synchronous lift and provide a method to construct a (there may be many) lifted system should one exist. We characterise the class of all systems that admit a synchronous lift by showing that they consist of fundamental vector fields induced by a Lie group action, a class of system we term fundamental systems. For fundamental systems we propose a simple synchronous observer design methodology, for which we show how correction terms can be discretised and combined easily, facilitating global characterisation of convergence and performance. Finally, we provide three examples to demonstrate the key concepts of synchrony, symmetry construction, and observer design for a fundamental system.
Alignment of large language models with constrained learning
Authors: Botong Zhang, Shuo Li, Ignacio Hounie, Osbert Bastani, Dongsheng Ding, Alejandro Ribeiro
Date: 2025-05-26 00:00:00
Tasks: Large Language Model

We study the problem of computing an optimal large language model (LLM) policy for a constrained alignment problem, where the goal is to maximize a primary reward objective while satisfying constraints on secondary utilities. Despite the popularity of Lagrangian-based LLM policy search in constrained alignment, iterative primal-dual methods often fail to converge, and non-iterative dual-based methods do not achieve optimality in the LLM parameter space. To address these challenges, we employ Lagrangian duality to develop an iterative dual-based alignment method that alternates between updating the LLM policy via Lagrangian maximization and updating the dual variable via dual descent. In theory, we characterize the primal-dual gap between the primal value in the distribution space and the dual value in the LLM parameter space. We further quantify the optimality gap of the learned LLM policies at near-optimal dual variables with respect to both the objective and the constraint functions. These results prove that dual-based alignment methods can find an optimal constrained LLM policy, up to an LLM parametrization gap. We demonstrate the effectiveness and merits of our approach through extensive experiments conducted on the PKU-SafeRLHF dataset.
FedORA: Resource Allocation for Federated Learning in ORAN using Radio Intelligent Controllers
Authors: Abdelaziz Salama, Mohammed M. H. Qazzaz, Syed Danial Ali Shah, Maryam Hafeez, Syed Ali Zaidi
Date: 2025-05-25 00:00:00
Tasks: Federated Learning, Reinforcement Learning (RL)

This work proposes an integrated approach for optimising Federated Learning (FL) communication in dynamic and heterogeneous network environments. Leveraging the modular flexibility of the Open Radio Access Network (ORAN) architecture and multiple Radio Access Technologies (RATs), we aim to enhance data transmission efficiency and mitigate client-server communication constraints within the FL framework. Our system employs a two-stage optimisation strategy using ORAN's rApps and xApps. In the first stage, Reinforcement Learning (RL) based rApp is used to dynamically select each user's optimal Radio Access Technology (RAT), balancing energy efficiency with network performance. In the second stage, a model-based xApp facilitates near-real-time resource allocation optimisation through predefined policies to achieve optimal network performance. The dynamic RAT selection and resource allocation capabilities enabled by ORAN and multi-RAT contribute to robust communication resilience in dynamic network environments. Our approach demonstrates competitive performance with low power consumption compared to other state-of-the-art models, showcasing its potential for real-time applications demanding both accuracy and efficiency. This robust and comprehensive framework, enabling clients to utilise available resources effectively, highlights the potential for scalable, collaborative learning applications prioritising energy efficiency and network performance.
An Autocovariance Least-Squares-Based Data-Driven Kalman Filter for Unknown Systems
Authors: Suyang Hu, Xiaoxu Lyu, Peihu Duan, Dawei Shi, Ling Shi
Date: 2025-05-25 00:00:00
Tasks: State Estimation

This article investigates the problem of data-driven state estimation for linear systems with both unknown system dynamics and noise covariances. We propose an Autocovariance Least-squares-based Data-driven Kalman Filter (ADKF), which provides a unified framework for simultaneous system identification and state estimation by utilizing pre-collected input-output trajectories and estimated initial states. Specifically, we design a SDP-based algorithm for estimating the noise covariances. We quantify the impact of model inaccuracy on noise covariances estimation using this identification algorithm, and introduce a feedback control mechanism for data collection to enhance the accuracy and stability of noise covariance estimation. The estimated noise covariances account for model inaccuracy, which are shown to be more suitable for state estimation. We also quantify the performance gap between the ADKF and the traditional Kalman filter with known system dynamics and noise covariances, showing that this gap decreases as the number and length of pre-collected trajectories increase. Finally, numerical simulations validate the robustness and effectiveness of the proposed ADKF.
Robust Stability Analysis of Positive Lure System with Neural Network Feedback
Authors: Hamidreza Montazeri Hedesh, Moh. Kamalul Wafi, Bahram Shafai, Milad Siami
Date: 2025-05-25 00:00:00

This paper investigates the robustness of the Lur'e problem under positivity constraints, drawing on results from the positive Aizerman conjecture and the robustness properties of Metzler matrices. Specifically, we consider a control system of Lur'e type in which not only the linear part includes parametric uncertainty but also the nonlinear sector bound is unknown. We investigate tools from positive linear systems to effectively solve the problems in complicated and uncertain nonlinear systems. By leveraging the positivity characteristic of the system, we derive an explicit formula for the stability radius of Lur'e systems. Furthermore, we extend our analysis to systems with neural network (NN) feedback loops. Building on this approach, we also propose a refinement method for sector bounds of feedforward neural networks (FFNNs). This study introduces a scalable and efficient approach for robustness analysis of both Lur'e and NN-controlled systems. Finally, the proposed results are supported by illustrative examples.