pwc-archive/papers-with-abstracts huggingface dataset:
Data-Driven Discrepancy Modeling in Higher-Dimensional State Space via Coprime Factorization
Authors: Sourav Sinha, Mazen Farhood
Date: 2025-02-23 00:00:00

This work provides a data-driven framework that combines coprime factorization with a lifting linearization technique to model the discrepancy between a nonlinear system and its nominal linear approximation using a linear time-invariant (LTI) state-space model in a higher-dimensional state space. In the proposed framework, the nonlinear system is represented in terms of the left coprime factors of the nominal linear system, along with perturbations modeled as stable, norm-bounded LTI systems in a higher-dimensional state space using a deep learning approach. Our method builds on a recently proposed parametrization for norm-bounded systems, enabling the simultaneous minimization of the H-infinity norm of the learned perturbations. We also provide a coprime factorization-based approach as an alternative to direct methods for learning lifted LTI approximations of nonlinear systems. In this approach, the LTI approximations are obtained by learning their left coprime factors, which remain stable even when the original system is unstable. The effectiveness of the proposed discrepancy modeling approach is demonstrated through multiple examples.
Equilibrium Unit Based Localized Affine Formation Maneuver for Multi-agent Systems
Authors: Cheng Zhu, Xiaotao Zhou, Bing Huang
Date: 2025-02-23 00:00:00

Current affine formation maneuver of multi-agent systems (MASs) relys on the affine localizability determined by generic assumption for nominal configuration and global construction manner. This does not live up to practical constraints of robot swarms. In this paper, an equilibrium unit based structure is proposed to achieve affine localizability. In an equilibrium unit, existence of non-zero weights between nodes is guaranteed and their summation is proved to be non-zero. To remove the generic assumption, a notion of layerable directed graph is introduced, based on which a sufficient condition associated equilibrium unit is presented to establish affine localizability condition. Within this framework, distributed local construction manner is performed by a designed equilibrium unit construction (EUC) method. With the help of localized communication criterion (LCC) and localized sensing based affine formation maneuver control (LSAFMC) protocol, self-reconstruction capability is possessed by MASs when nodes are added to or removed from the swarms.
Robust Nonlinear Data-Driven Predictive Control for Mixed Vehicle Platoons via Koopman Operator and Reachability Analysis
Authors: Shuai Li, Jiawei Wang, Kaidi Yang, Qing Xu, Jianqiang Wang, Keqiang Li
Date: 2025-02-23 00:00:00

Mixed vehicle platoons, comprising connected and automated vehicles (CAVs) and human-driven vehicles (HDVs), hold significant potential for enhancing traffic performance. Most existing research assumes linear system dynamics and often ignores the impact of critical factors such as noise, disturbances, and attacks, which are inherent to real-world scenarios. To address these limitations, we propose a Robust Nonlinear Data-Driven Predictive Control (RNDDPC) framework that ensures safe and optimal control under uncertain and adverse conditions. By utilizing Koopman operator theory, we map the system's nonlinear dynamics into a higher-dimensional space, constructing a Koopman-based linear predictor that approximates the behavior of the original nonlinear system. To mitigate modeling errors associated with this predictor, we introduce a data-driven reachable set analysis technique that performs secondary learning using matrix zonotope sets, generating a reachable set predictor for over-approximation of the future states of the underlying system. Then, we formulate the RNDDPC optimization problem and solve it in a receding horizon manner for robust control inputs. Extensive simulations demonstrate that the proposed framework significantly outperforms baseline methods in tracking performance under noise, disturbances, and attacks.
Physics-Informed Gradient Estimation for Accelerating Deep Learning based AC-OPF
Authors: Kejun Chen, Shourya Bose, Yu Zhang
Date: 2025-02-22 00:00:00
Tasks: Data Augmentation, Deep Learning

The optimal power flow (OPF) problem can be rapidly and reliably solved by employing responsive online solvers based on neural networks. The dynamic nature of renewable energy generation and the variability of power grid conditions necessitate frequent neural network updates with new data instances. To address this need and reduce the time required for data preparation time, we propose a semi-supervised learning framework aided by data augmentation. In this context, ridge regression replaces the traditional solver, facilitating swift prediction of optimal solutions for the given input load demands. Additionally, to accelerate the backpropagation during training, we develop novel batch-mean gradient estimation approaches along with a reduced branch set to alleviate the complexity of gradient computation. Numerical simulations demonstrate that our neural network, equipped with the proposed gradient estimators, consistently achieves feasible and near-optimal solutions. These results underline the effectiveness of our approach for practical implementation in real-time OPF applications.
Demand Forecasting for Electric Vehicle Charging Stations using Multivariate Time-Series Analysis
Authors: Saba Sanami, Hesam Mosalli, Yu Yang, Hen-Geul Yeh, Amir G. Aghdam
Date: 2025-02-22 00:00:00
Tasks: Decision Making, Demand Forecasting, Time Series, Time Series Analysis

As the number of electric vehicles (EVs) continues to grow, the demand for charging stations is also increasing, leading to challenges such as long wait times and insufficient infrastructure. High-precision forecasting of EV charging demand is crucial for efficient station management, to address some of these challenges. This paper presents an approach to predict the charging demand at 15-minute intervals for the day ahead using a multivariate long short-term memory (LSTM) network with an attention mechanism. Additionally, the model leverages explainable AI techniques to evaluate the influence of various factors on the predictions, including weather conditions, day of the week, month, and any holiday. SHapley Additive exPlanations (SHAP) are used to quantify the contribution of each feature to the final forecast, providing deeper insights into how these factors affect prediction accuracy. As a result, the framework offers enhanced decision-making for infrastructure planning. The efficacy of the proposed method is demonstrated by simulations using the test data collected from the EV charging stations at California State University, Long Beach.
Pseudo-Measurement Enhancement in Power Distribution Systems
Authors: Tao Xu, Kaiqi Wang, Jiadong Zhang, Ji Qiao, Zixuan Zhao, Hong Zhu, Kai Sun
Date: 2025-02-22 00:00:00
Tasks: Computational Efficiency

With the rapid development of smart distribution networks (DNs), the integrity and accuracy of grid measurement data are crucial to the safety and stability of the entire system. However, the quality of the user power consumption data cannot be guaranteed during the collection and transmission process. To this end, this paper proposes a low-rank tensor completion model based on CANDECOMP/PARAFAC decomposition (CPD-LRTC) to enhance the quality of the measurement data of the DNs. Firstly, the causes and the associated characteristics of the missing data are analyzed, and a third-order standard tensor is constructed as a mathematical model of the measurement data of the DN. Then, a completion model is established based on the characteristics of measurement data and the low rank of the completion tensor, and the alternating direction method of multipliers (ADMM) is used to solve it iteratively. Finally, the proposed model is verified through two case studies, the completion accuracy, the computational efficiency, and the memory usage are compared to traditional methods.
Orthogonality Analysis in LoRa Uplink Satellite Communications Affected by Doppler Effect
Authors: Jikang Deng, Fatma Benkhelifa, Mohamed-Slim Alouini
Date: 2025-02-22 00:00:00

This paper provides, for the first time, analytical expressions for the Long-Range (LoRa) waveform and cross-correlation in both continuous and discrete time domains under the Doppler effect in satellite communication. We propose the concept and formulas of the shared visibility window for satellites toward two ground devices. Our analysis covers cross-correlation results with varying spreading factors (SF) for no-Doppler and with-Doppler cases. We find the maximum cross-correlation with different SFs and the mean cross-correlation are immune to the Doppler effect. However, the maximum cross-correlation with the same SFs is only immune to high Doppler shift, with its value fluctuating between 0.6 and 1 under high Doppler rate. We interpret this fluctuation by introducing the relationship between transmission start time and cross-correlation. We provide a parameter analysis for orbit height, ground device distance, and inclination angle. Additionally, we analyze the bit error rate (BER) for LoRa signals and observe worse performance under high Doppler shift or interference with same SF. Increasing the SNR or the SIR improves the BER only when Doppler effect is below a frequency threshold. Notably, under Doppler effect, the performance behaviors of BER no longer align with those of maximum cross-correlation. Finally, our results lead to two recommendations: 1) To mitigate Doppler impact on cross-correlation, we recommend utilizing low SFs, high orbit height, short ground device distance, and the transmission start time with high Doppler shift; 2) To mitigate Doppler impact on BER, we recommend employing low SFs, high bandwidth, and transmission start time with high Doppler rate. These conflicting recommendations regarding transmission start time highlight the necessity of Doppler shift compensation techniques to help operate LoRa in space properly.
Impact Analysis of Utility-Scale Energy Storage on the ERCOT Grid in Reducing Renewable Generation Curtailments and Emissions
Authors: Cody Buehner, Sharaf K. Magableh, Oraib Dawaghreh, Caisheng Wang
Date: 2025-02-21 00:00:00

This paper explores the solutions for minimizing renewable energy (RE) curtailment in the Texas Electric Reliability Council of Texas (ERCOT) grid. By utilizing current and future planning data from ERCOT and the System Advisor Model from the National Renewable Energy Laboratory, we examine how future renewable energy (RE) initiatives, combined with utility-scale energy storage, can reduce CO2 emissions while reshaping Texas's energy mix. The study projects the energy landscape from 2023 to 2033, considering the planned phase-out of fossil fuel plants and the integration of new wind/solar projects. By comparing emissions under different load scenarios, with and without storage, we demonstrate storage's role in optimizing RE utilization. The findings of this paper provide actionable guidance for energy stakeholders, underscoring the need to expand wind and solar projects with strategic storage solutions to maximize Texas's RE capacity and substantially reduce CO2 emissions.
Parameter Estimation of the Network of FitzHugh-Nagumo Neurons Based on the Speed-Gradient and Filtering
Authors: Aleksandra Rybalko, Alexander Fradkov
Date: 2025-02-21 00:00:00
Tasks: EEG, parameter estimation

The paper addresses the problem of parameter estimation (or identification) in dynamical networks composed of an arbitrary number of FitzHugh-Nagumo neuron models with diffusive couplings between each other. It is assumed that only the membrane potential of each model is measured, while the other state variable and all derivatives remain unmeasured. Additionally, potential measurement errors in the membrane potential due to sensor imprecision are considered. To solve this problem, firstly, the original FitzHugh-Nagumo network is transformed into a linear regression model, where the regressors are obtained by applying a filter-differentiator to specific combinations of the measured variables. Secondly, the speed-gradient method is applied to this linear model, leading to the design of an identification algorithm for the FitzHugh-Nagumo neural network. Sufficient conditions for the asymptotic convergence of the parameter estimates to their true values are derived for the proposed algorithm. Parameter estimation for a network of five interconnected neurons is demonstrated through computer simulation. The results confirm that the sufficient conditions are satisfied in the numerical experiments conducted. Furthermore, the algorithm's capabilities for adjusting the identification accuracy and time are investigated. The proposed approach has potential applications in nervous system modeling, particularly in the context of human brain modeling. For instance, EEG signals could serve as the measured variables of the network, enabling the integration of mathematical neural models with empirical data collected by neurophysiologists.
Utilizing Machine Learning to Predict Host Stars and the Key Elemental Abundances of Small Planets
Authors: Amílcar R. Torres-Quijano, Natalie R. Hinkel, Caleb H. Wheeler III, Patrick A. Young, Luan Ghezzi, Augusto P. Baldo
Date: 2025-02-24 00:00:00

Stars and their associated planets originate from the same cloud of gas and dust, making a star's elemental composition a valuable indicator for indirectly studying planetary compositions. While the connection between a star's iron (Fe) abundance and the presence of giant exoplanets is established (e.g. Gonzalez 1997; Fischer & Valenti 2005), the relationship with small planets remains unclear. The elements Mg, Si, and Fe are important in forming small planets. Employing machine learning algorithms like XGBoost, trained on the abundances (e.g., the Hypatia Catalog, Hinkel et al. 2014) of known exoplanet-hosting stars (NASA Exoplanet Archive), allows us to determine significant "features" (abundances or molar ratios) that may indicate the presence of small planets. We test on three groups of exoplanets: (a) all small, R$_{P}$ $<$ 3.5 $R_{\oplus}$, (b) sub-Neptunes, 2.0 $R_{\oplus}$ $<$ R$_{P}$ $<$ 3.5 $R_{\oplus}$, and (c) super-Earths, 1.0 $R_{\oplus}$ $<$ R$_{P}$ $<$ 2.0 $R_{\oplus}$ -- each subdivided into 7 ensembles to test different combinations of features. We created a list of stars with $\geq90\%$ probability of hosting small planets across all ensembles and experiments ("overlap stars"). We found abundance trends for stars hosting small planets, possibly indicating star-planet chemical interplay during formation. We also found that Na and V are key features regardless of planetary radii. We expect our results to underscore the importance of elements in exoplanet formation and machine learning's role in target selection for future NASA missions: e.g., the James Webb Space Telescope (JWST), Nancy Grace Roman Space Telescope (NGRST), Habitable Worlds Observatory (HWO) -- all of which are aimed at small planet detection.
Asteroid shape inversion with light curves using deep learning
Authors: YiJun Tang, ChenChen Ying, ChengZhe Xia, XiaoMing Zhang, XiaoJun Jiang
Date: 2025-02-23 00:00:00
Tasks: Deep Learning

Asteroid shape inversion using photometric data has been a key area of study in planetary science and astronomical research.However, the current methods for asteroid shape inversion require extensive iterative calculations, making the process time-consuming and prone to becoming stuck in local optima. We directly established a mapping between photometric data and shape distribution through deep neural networks. In addition, we used 3D point clouds to represent asteroid shapes and utilized the deviation between the light curves of non-convex asteroids and their convex hulls to predict the concave areas of non-convex asteroids. We compared the results of different shape models using the Chamfer distance between traditional methods and ours and found that our method performs better, especially when handling special shapes. For the detection of concave areas on the convex hull, the intersection over union (IoU) of our predictions reached 0.89. We further validated this method using observational data from the Lowell Observatory to predict the convex shapes of the asteroids 3337 Milo and 1289 Kuta, and conducted light curve fitting experiments. The experimental results demonstrated the robustness and adaptability of the method
FLARE: A Framework for Stellar Flare Forecasting using Stellar Physical Properties and Historical Records
Authors: Bingke Zhu, Xiaoxiao Wang, Minghui Jia, Yihan Tao, Xiao Kong, Ali Luo, Yingying Chen, Ming Tang, Jinqiao Wang
Date: 2025-02-25 00:00:00

Stellar flare events are critical observational samples for astronomical research; however, recorded flare events remain limited. Stellar flare forecasting can provide additional flare event samples to support research efforts. Despite this potential, no specialized models for stellar flare forecasting have been proposed to date. In this paper, we present extensive experimental evidence demonstrating that both stellar physical properties and historical flare records are valuable inputs for flare forecasting tasks. We then introduce FLARE (Forecasting Light-curve-based Astronomical Records via features Ensemble), the first-of-its-kind large model specifically designed for stellar flare forecasting. FLARE integrates stellar physical properties and historical flare records through a novel Soft Prompt Module and Residual Record Fusion Module. Our experiments on the publicly available Kepler light curve dataset demonstrate that FLARE achieves superior performance compared to other methods across all evaluation metrics. Finally, we validate the forecast capability of our model through a comprehensive case study.
Inverse Materials Design by Large Language Model-Assisted Generative Framework
Authors: Yun Hao, Che Fan, Beilin Ye, Wenhao Lu, Zhen Lu, Peilin Zhao, Zhifeng Gao, Qingyao Wu, Yanhui Liu, Tongqi Wen
Date: 2025-02-25 00:00:00
Tasks: Diversity, Language Modeling, Language Modelling, Large Language Model

Deep generative models hold great promise for inverse materials design, yet their efficiency and accuracy remain constrained by data scarcity and model architecture. Here, we introduce AlloyGAN, a closed-loop framework that integrates Large Language Model (LLM)-assisted text mining with Conditional Generative Adversarial Networks (CGANs) to enhance data diversity and improve inverse design. Taking alloy discovery as a case study, AlloyGAN systematically refines material candidates through iterative screening and experimental validation. For metallic glasses, the framework predicts thermodynamic properties with discrepancies of less than 8% from experiments, demonstrating its robustness. By bridging generative AI with domain knowledge and validation workflows, AlloyGAN offers a scalable approach to accelerate the discovery of materials with tailored properties, paving the way for broader applications in materials science.
A Machine Learning Approach for Design of Frequency Selective Surface based Radar Absorbing Material via Image Prediction
Authors: Vijay Kumar Sutrakar, Anjana P K, Sajal Kesharwani, Siddharth Bisariya
Date: 2025-02-24 00:00:00
Tasks: regression

The paper presents an innovative methodology for designing frequency selective surface (FSS) based radar absorbing materials using machine learning (ML) technique. In conventional electromagnetic design, unit cell dimensions of FSS are used as input and absorption coefficient is then predicted for a given design. In this paper, absorption coefficient is considered as input to ML model and image of FSS unit cell is predicted. Later, this image is used for generating the FSS unit cell parameters. Eleven different ML models are studied over a wide frequency band of 1GHz to 30GHz. Out of which six ML models (i.e. (a) Random Forest classification, (b) K- Neighbors Classification, (c) Grid search regression, (d) Random Forest regression, (e) Decision tree classification, and (f) Decision tree regression) show training accuracy more than 90%. The absorption coefficients with varying frequencies of these predicted images are subsequently evaluated using commercial electromagnetic solver. The performance of these ML models is encouraging, and it can be used for accelerating design and optimization of high performance FSS based radar absorbing material for advanced electromagnetic applications in future.
Controlling dynamics of stochastic systems with deep reinforcement learning
Authors: Ruslan Mukhamadiarov
Date: 2025-02-25 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning

A properly designed controller can help improve the quality of experimental measurements or force a dynamical system to follow a completely new time-evolution path. Recent developments in deep reinforcement learning have made steep advances toward designing effective control schemes for fairly complex systems. However, a general simulation scheme that employs deep reinforcement learning for exerting control in stochastic systems is yet to be established. In this paper, we attempt to further bridge a gap between control theory and deep reinforcement learning by proposing a simulation algorithm that allows achieving control of the dynamics of stochastic systems through the use of trained artificial neural networks. Specifically, we use agent-based simulations where the neural network plays the role of the controller that drives local state-to-state transitions. We demonstrate the workflow and the effectiveness of the proposed control methods by considering the following two stochastic processes: particle coalescence on a lattice and a totally asymmetric exclusion process.
Why do financial prices exhibit Brownian motion despite predictable order flow?
Authors: Yuki Sato, Kiyoshi Kanazawa
Date: 2025-02-25 00:00:00

In financial market microstructure, there are two enigmatic empirical laws: (i) the market-order flow has predictable persistence due to metaorder splitters by institutional investors, well formulated as the Lillo-Mike-Farmer model. However, this phenomenon seems paradoxical given the diffusive and unpredictable price dynamics; (ii) the price impact $I(Q)$ of a large metaorder $Q$ follows the square-root law, $I(Q)\propto \sqrt{Q}$. Here we theoretically reveal why price dynamics follows Brownian motion despite predictable order flow by unifying these enigmas. We generalize the Lillo-Mike-Farmer model to nonlinear price-impact dynamics, which is mapped to an exactly solvable L\'evy-walk model. Our exact solution shows that the price dynamics remains diffusive under the square-root law, even under persistent order flow. This work illustrates the crucial role of the square-root law in mitigating large price movements by large metaorders, thereby leading to the Brownian price dynamics, consistently with the efficient market hypothesis over long timescales.
What is a chemostat? Insights from hybrid dynamics and stochastic thermodynamics
Authors: Benedikt Remlein, Massimiliano Esposito, Francesco Avanzini
Date: 2025-02-24 00:00:00

At the microscopic scale, open chemical reaction networks are described by stochastic reactions that follow mass-action kinetics and are coupled to chemostats. We show that closed chemical reaction networks -- with specific stoichiometries imposed by mass-action kinetics -- behave like open ones in the limit where the abundances of a subset of species become macroscopic, thus playing the role of chemostats. We prove that this limit is thermodynamically consistent by recovering the local detailed balance condition of open chemical reaction networks and deriving the proper expression of the entropy production rate. In particular, the entropy production rate features two contributions: one accounting for the dissipation of the stochastic reactions, the other for the dissipation of continuous reactions controlling the chemostats. Finally, we illustrate our results for two prototypical examples.