pwc-archive/papers-with-abstracts huggingface dataset:
Almost Sure Convergence of Networked Policy Gradient over Time-Varying Networks in Markov Potential Games
Authors: Sarper Aydin, Ceyhun Eksin
Date: 2024-10-26 00:00:00

We propose networked policy gradient play for solving Markov potential games including continuous action and state spaces. In the decentralized algorithm, agents sample their actions from parametrized and differentiable policies that depend on the current state and other agents' policy parameters. During training, agents estimate their gradient information through two consecutive episodes, generating unbiased estimators of reward and policy score functions. Using this information, agents compute the stochastic gradients of their policy functions and update their parameters accordingly. Additionally, they update their estimates of other agents' policy parameters based on the local estimates received through a time-varying communication network. In Markov potential games, there exists a potential value function among agents with gradients corresponding to the gradients of local value functions. Using this structure, we prove the almost sure convergence of joint policy parameters to stationary points of the potential value function. We also show that the convergence rate of the networked policy gradient algorithm is $\mathcal{O}(1/\epsilon^2)$. Numerical experiments on a dynamic multi-agent newsvendor problem verify the convergence of local beliefs and gradients. It further shows that networked policy gradient play converges as fast as independent policy gradient updates, while collecting higher rewards.
Optimal demand-responsive connector design: Comparing fully-flexible routing and semi-flexible routing strategies
Authors: Li Zhen, Weihua Gu
Date: 2024-10-26 00:00:00

Demand-responsive connector (DRC) services are increasingly recognized for their convenience, comfort, and efficiency, offering seamless integrations between travelers' origins/destinations and major transportation hubs such as rail stations. Past analytical models for DRC optimization often failed to distinguish between two commonly used DRC operating strategies: (i) the "fully-flexible routing" strategy, where a vehicle serves only the requests received before its dispatch through an optimal tour, and (ii) the "semi-flexible routing" strategy, where a vehicle follows a predefined path through a swath to serve requests received en route. Additionally, these models often adopted oversimplified approaches for estimating local tour lengths and capturing the stochastic nature of demand. This paper distinctly identifies and analyzes the two DRC operating strategies, developing analytical models for each that accurately incorporate the second-order effects of stochastic demand and utilize refined local tour length formulas. Numerical experiments demonstrate that our models reduce cost estimation errors to within 2% for fully-flexible routing and to 0.25% for semi-flexible routing, a significant improvement over the previous errors of 8-12% and 6.3%, respectively. These enhanced models allow for more precise determination of critical demand densities for selecting between the two DRC strategies and the fixed-route feeder service. Our extensive numerical analysis offers many insights, particularly highlighting the transition from fully-flexible to semi-flexible routing as demand and region size increase, before ultimately shifting to fixed-route service. Additionally, zoning is identified as pivotal in DRC service design, with fully-flexible routing favoring square-shaped zones and semi-flexible routing preferring elongated rectangular zones.
Duality of Stochastic Observability and Constructability and Links to Fisher Information
Authors: Burak Boyacıoğlu, Floris van Breugel
Date: 2024-10-25 00:00:00
Tasks: Relation

Given a set of measurements, observability characterizes the distinguishability of a system's initial state, whereas constructability focuses on the final state in a trajectory. In the presence of process and/or measurement noise, the Fisher information matrices with respect to the initial and final states$\unicode{x2013}$equivalent to the stochastic observability and constructability Gramians$\unicode{x2013}$bound the performance of corresponding estimators through the Cram\'er-Rao inequality. This letter establishes a connection between stochastic observability and constructability of discrete-time linear systems and provides a more numerically robust way for calculating the stochastic observability Gramian. We define a dual system and show that the dual system's stochastic constructability is equivalent to the original system's stochastic observability, and vice versa. This duality enables the interchange of theorems and tools for observability and constructability. For example, we use this result to translate an existing recursive formula for the stochastic constructability Gramian into a formula for recursively calculating the stochastic observability Gramian for both time-varying and time-invariant systems, where this sequence converges for the latter. Finally, we illustrate the robustness of our formula compared to existing (non-recursive) formulas through a numerical example.
Implementing Deep Reinforcement Learning-Based Grid Voltage Control in Real-World Power Systems: Challenges and Insights
Authors: Di Shi, Qiang Zhang, Mingguo Hong, Fengyu Wang, Slava Maslennikov, Xiaochuan Luo, Yize Chen
Date: 2024-10-24 00:00:00
Tasks: Deep Reinforcement Learning, Management

Deep reinforcement learning (DRL) holds significant promise for managing voltage control challenges in simulated power grid environments. However, its real-world application in power system operations remains underexplored. This study rigorously evaluates DRL's performance and limitations within actual operational contexts by utilizing detailed experiments across the IEEE 14-bus system, Illinois 200-bus system, and the ISO New England node-breaker model. Our analysis critically assesses DRL's effectiveness for grid control from a system operator's perspective, identifying specific performance bottlenecks. The findings provide actionable insights that highlight the necessity of advancing AI technologies to effectively address the growing complexities of modern power systems. This research underscores the vital role of DRL in enhancing grid management and reliability.
Data-Driven Transient Stability Assessment of Power Systems with a Novel GHM-Enhanced CatBoost Algorithm
Authors: Zheheng Wang
Date: 2024-10-24 00:00:00
Tasks: Feature Importance

This study introduces an advanced transient stability assessment (TSA) method for power systems, addressing the challenges of sample class imbalance and data noise through a novel CatBoost algorithm framework. By implementing a Gradient Harmonizing Mechanism (GHM), this method adjusts the gradient norm distribution across samples by incorporating a coordination parameter for each, thus optimizing the gradient weights for various sample types. This enhancement enables more effective training of the CatBoost algorithm, reducing the negative impacts of class imbalance and noise, and enhancing algorithmic performance. Additionally, the feature importance functionality of the CatBoost framework guides the placement of phasor measurement units, promoting economical operation of the power system. Numerical results from the New England 10-machine 39-bus system demonstrate the superior versatility, reduced application cost, and lower maintenance expenses of the proposed method compared to existing techniques.
Impact of uncertainties on the Stability Lobe Diagram for vibration evaluation in milling
Authors: Diego R Villacreses Naranjo, Frédéric Vignat, Nicolas Béraud
Date: 2024-10-23 00:00:00
Tasks: Decision Making

Despite being the subject of study for several years, excessive vibration persists in the machining of metal parts. In this context, the Stability Lobe Diagram (SLD) is presented as a viable tool to mitigate this problem as a function of axial depth of cut and spindle speed. However, its accurate construction is subject to the consideration of multiple parameters and models, whose application may be affected by certain inherent uncertainties. In turn, this impacts its accuracy, especially in the stability and instability regions. The present study aims to characterize these uncertainties, analyze their influence on the SLD, and propose strategies for their reduction. Ultimately, the goal is to facilitate the user's decision-making when choosing the trajectory generation parameters.
Co-evolutionary control of a class of coupled mixed-feedback systems
Authors: Luis Guillermo Venegas-Pineda, Hildeberto Jardón-Kojakhmetov, Ming Cao
Date: 2024-10-22 00:00:00

Oscillatory behavior is ubiquitous in many natural and engineered systems, often emerging through self-regulating mechanisms. In this paper, we address the challenge of stabilizing a desired oscillatory pattern in a networked system where neither the internal dynamics nor the interconnections can be changed. To achieve this, we propose two distinct control strategies. The first requires the full knowledge of the system generating the desired oscillatory pattern, while the second only needs local error information. In addition, the controllers are implemented as co-evolutionary, or adaptive, rules of some edges in an extended plant-controller network. We validate our approach in several insightful scenarios, including synchronization and systems with time-varying network structures.
Artificial intelligence for partial differential equations in computational mechanics: A review
Authors: Yizheng Wang, Jinshuai Bai, Zhongya Lin, Qimin Wang, Cosmin Anitescu, Jia Sun, Mohammad Sadegh Eshaghi, Yuantong Gu, Xi-Qiao Feng, Xiaoying Zhuang, Timon Rabczuk, Yinghua Liu
Date: 2024-10-21 00:00:00
Tasks: Operator learning

In recent years, Artificial intelligence (AI) has become ubiquitous, empowering various fields, especially integrating artificial intelligence and traditional science (AI for Science: Artificial intelligence for science), which has attracted widespread attention. In AI for Science, using artificial intelligence algorithms to solve partial differential equations (AI for PDEs: Artificial intelligence for partial differential equations) has become a focal point in computational mechanics. The core of AI for PDEs is the fusion of data and partial differential equations (PDEs), which can solve almost any PDEs. Therefore, this article provides a comprehensive review of the research on AI for PDEs, summarizing the existing algorithms and theories. The article discusses the applications of AI for PDEs in computational mechanics, including solid mechanics, fluid mechanics, and biomechanics. The existing AI for PDEs algorithms include those based on Physics-Informed Neural Networks (PINNs), Deep Energy Methods (DEM), Operator Learning, and Physics-Informed Neural Operator (PINO). AI for PDEs represents a new method of scientific simulation that provides approximate solutions to specific problems using large amounts of data, then fine-tuning according to specific physical laws, avoiding the need to compute from scratch like traditional algorithms. Thus, AI for PDEs is the prototype for future foundation models in computational mechanics, capable of significantly accelerating traditional numerical algorithms.
Evaluation of Peak Shaving Using Thermal Energy Storage in a Validated CHP and District Energy Model
Authors: Michael Huylo, Sina Taheri, Atila Novoselac
Date: 2024-10-19 00:00:00
Tasks: Scheduling

There is currently a large federal effort to decarbonize the country's electrical grid as part of the clean energy transition. The elimination of fossil fuel fired systems, and their replacement with intermittent renewable sources and other electric equipment will require better load management techniques to ensure a reliable grid. One strategy for maintaining electric grid reliability utilizes peak shaving. Buildings, accounting for 40% of energy use in the United States, can account for an even higher percentage of energy during peak periods driven by high air conditioning loads during the summer, especially in hotter climes such as Austin, Texas. Many previous studies have modeled the effectiveness of building HVAC demand response methods such as temperature setpoint manipulation, pre-cooling, ventilation scheduling, and thermal energy storage. Thermal storage systems, due to their larger energy capacities, have been shown to be most promising for peak shaving. However, there is a lack of work integrating chilled water energy storage models with validated microgrid-district energy system models to fully capture the dynamics of the proposed strategies. Previously, a validated system model for power generation and heating was developed for the University of Texas at Austin (UT Austin). A new validated model integrates the 65 MW combined heat and power plant (CHP), with the campus' 45,000 ton district cooling system, as well as two chilled water storage tanks. While the existing campus system currently utilizes an operator driven peak shaving strategy utilizing thermal storage, optimization results show that there is room for further improvement and energy savings. The presented results quantify the peak shaving in MW and provide a foundation for further analysis.
ControlAgent: Automating Control System Design via Novel Integration of LLM Agents and Domain Expertise
Authors: Xingang Guo, Darioush Keivan, Usman Syed, Lianhui Qin, huan zhang, Geir Dullerud, Peter Seiler, Bin Hu
Date: 2024-10-17 00:00:00
Tasks: Specificity

Control system design is a crucial aspect of modern engineering with far-reaching applications across diverse sectors including aerospace, automotive systems, power grids, and robotics. Despite advances made by Large Language Models (LLMs) in various domains, their application in control system design remains limited due to the complexity and specificity of control theory. To bridge this gap, we introduce ControlAgent, a new paradigm that automates control system design via novel integration of LLM agents and control-oriented domain expertise. ControlAgent encodes expert control knowledge and emulates human iterative design processes by gradually tuning controller parameters to meet user-specified requirements for stability, performance, and robustness. ControlAgent integrates multiple collaborative LLM agents, including a central agent responsible for task distribution and task-specific agents dedicated to detailed controller design for various types of systems and requirements. ControlAgent also employs a Python computation agent that performs complex calculations and controller evaluations based on standard design information provided by task-specified LLM agents. Combined with a history and feedback module, the task-specific LLM agents iteratively refine controller parameters based on real-time feedback from prior designs. Overall, ControlAgent mimics the design processes used by (human) practicing engineers, but removes all the human efforts and can be run in a fully automated way to give end-to-end solutions for control system design with user-specified requirements. To validate ControlAgent's effectiveness, we develop ControlEval, an evaluation dataset that comprises 500 control tasks with various specific design goals. The effectiveness of ControlAgent is demonstrated via extensive comparative evaluations between LLM-based and traditional human-involved toolbox-based baselines.
RESISTO Project: Automatic detection of operation temperature anomalies for power electric transformers using thermal imaging
Authors: David López-García, Fermín Segovia, Jacob Rodríguez-Rivero, Javier Ramírez, David Pérez, Raúl Serrano, Juan Manuel Górriz
Date: 2024-10-16 00:00:00

The RESISTO project represents a pioneering initiative in Europe aimed at enhancing the resilience of the power grid through the integration of advanced technologies. This includes artificial intelligence and thermal surveillance systems to mitigate the impact of extreme meteorological phenomena. RESISTO endeavors to predict, prevent, detect, and recover from weather-related incidents, ultimately enhancing the quality of service provided and ensuring grid stability and efficiency in the face of evolving climate challenges. In this study, we introduce one of the fundamental pillars of the project: a monitoring system for the operating temperature of different regions within power transformers, aiming to detect and alert early on potential thermal anomalies. To achieve this, a distributed system of thermal cameras for real-time temperature monitoring has been deployed in The Do\~nana National Park, alongside servers responsible for the storing, analyzing, and alerting of any potential thermal anomalies. An adaptive prediction model was developed for temperature forecasting, which learns online from the newly available data. In order to test the long-term performance of the proposed solution, we generated a synthetic temperature database for the whole of the year 2022. Overall, the proposed system exhibits promising capabilities in predicting and detecting thermal anomalies in power electric transformers, showcasing potential applications in enhancing grid reliability and preventing equipment failures.
A Cosmic-Scale Benchmark for Symmetry-Preserving Data Processing
Authors: Julia Balla, Siddharth Mishra-Sharma, Carolina Cuesta-Lazaro, Tommi Jaakkola, Tess Smidt
Date: 2024-10-27 00:00:00

Efficiently processing structured point cloud data while preserving multiscale information is a key challenge across domains, from graphics to atomistic modeling. Using a curated dataset of simulated galaxy positions and properties, represented as point clouds, we benchmark the ability of graph neural networks to simultaneously capture local clustering environments and long-range correlations. Given the homogeneous and isotropic nature of the Universe, the data exhibits a high degree of symmetry. We therefore focus on evaluating the performance of Euclidean symmetry-preserving ($E(3)$-equivariant) graph neural networks, showing that they can outperform non-equivariant counterparts and domain-specific information extraction techniques in downstream performance as well as simulation-efficiency. However, we find that current architectures fail to capture information from long-range correlations as effectively as domain-specific baselines, motivating future work on architectures better suited for extracting long-range information.
SIGMA: Single Interpolated Generative Model for Anomalies
Authors: Ranit Das, David Shih
Date: 2024-10-27 00:00:00
Tasks: Anomaly Detection, model

A key step in any resonant anomaly detection search is accurate modeling of the background distribution in each signal region. Data-driven methods like CATHODE accomplish this by training separate generative models on the complement of each signal region, and interpolating them into their corresponding signal regions. Having to re-train the generative model on essentially the entire dataset for each signal region is a major computational cost in a typical sliding window search with many signal regions. Here, we present SIGMA, a new, fully data-driven, computationally-efficient method for estimating background distributions. The idea is to train a single generative model on all of the data and interpolate its parameters in sideband regions in order to obtain a model for the background in the signal region. The SIGMA method significantly reduces the computational cost compared to previous approaches, while retaining a similar high quality of background modeling and sensitivity to anomalous signals.
Recommendations for Comprehensive and Independent Evaluation of Machine Learning-Based Earth System Models
Authors: Paul A. Ullrich, Elizabeth A. Barnes, William D. Collins, Katherine Dagon, Shiheng Duan, Joshua Elms, Jiwoo Lee, L. Ruby Leung, Dan Lu, Maria J. Molina, Travis A. O'Brien, Finn O. Rebassoo
Date: 2024-10-24 00:00:00
Tasks: Weather Forecasting

Machine learning (ML) is a revolutionary technology with demonstrable applications across multiple disciplines. Within the Earth science community, ML has been most visible for weather forecasting, producing forecasts that rival modern physics-based models. Given the importance of deepening our understanding and improving predictions of the Earth system on all time scales, efforts are now underway to develop forecasting models into Earth-system models (ESMs), capable of representing all components of the coupled Earth system (or their aggregated behavior) and their response to external changes. Modeling the Earth system is a much more difficult problem than weather forecasting, not least because the model must represent the alternate (e.g., future) coupled states of the system for which there are no historical observations. Given that the physical principles that enable predictions about the response of the Earth system are often not explicitly coded in these ML-based models, demonstrating the credibility of ML-based ESMs thus requires us to build evidence of their consistency with the physical system. To this end, this paper puts forward five recommendations to enhance comprehensive, standardized, and independent evaluation of ML-based ESMs to strengthen their credibility and promote their wider use.
Evaluating Deep Learning Approaches for Predictions in Unmonitored Basins with Continental-scale Stream Temperature Models
Authors: Jared D. Willard, Fabio Ciulla, Helen Weierbach, Vipin Kumar, Charuleka Varadharajan
Date: 2024-10-23 00:00:00

The prediction of streamflows and other environmental variables in unmonitored basins is a grand challenge in hydrology. Recent machine learning (ML) models can harness vast datasets for accurate predictions at large spatial scales. However, there are open questions regarding model design and data needed for inputs and training to improve performance. This study explores these questions while demonstrating the ability of deep learning models to make accurate stream temperature predictions in unmonitored basins across the conterminous United States. First, we compare top-down models that utilize data from a large number of basins with bottom-up methods that transfer ML models built on local sites, reflecting traditional regionalization techniques. We also evaluate an intermediary grouped modeling approach that categorizes sites based on regional co-location or similarity of catchment characteristics. Second, we evaluate trade-offs between model complexity, prediction accuracy, and applicability for more target locations by systematically removing inputs. We then examine model performance when additional training data becomes available due to reductions in input requirements. Our results suggest that top-down models significantly outperform bottom-up and grouped models. Moreover, it is possible to get acceptable accuracy by reducing both dynamic and static inputs enabling predictions for more sites with lower model complexity and computational needs. From detailed error analysis, we determined that the models are more accurate for sites primarily controlled by air temperatures compared to locations impacted by groundwater and dams. By addressing these questions, this research offers a comprehensive perspective on optimizing ML model design for accurate predictions in unmonitored regions.