{"total":54,"rows":[{"id":"2407.17611","title":"Adaptive Training of Grid-Dependent Physics-Informed Kolmogorov-Arnold Networks","created":"2024-07-24","cited_by_count":38,"area":null},{"id":"2304.11726","title":"End-to-End Feasible Optimization Proxies for Large-Scale Economic Dispatch","created":"2023-04-23","cited_by_count":35,"area":null},{"id":"2205.10129","title":"Topology-aware Graph Neural Networks for Learning Feasible and Adaptive ac-OPF Solutions","created":"2022-05-16","cited_by_count":28,"area":null},{"id":"2407.09434","title":"Foundation Models for the Electric Power Grid","created":"2024-07-12","cited_by_count":28,"area":null},{"id":"2508.10480","title":"Pinet: Optimizing hard-constrained neural networks with orthogonal projection layers","created":"2025-08-14","cited_by_count":19,"area":null},{"id":"2301.08840","title":"Compact Optimization Learning for AC Optimal Power Flow","created":"2023-01-21","cited_by_count":11,"area":null},{"id":"2310.02969","title":"Dual Conic Proxies for AC Optimal Power Flow","created":"2023-10-04","cited_by_count":11,"area":null},{"id":"2203.07505","title":"Closing the Loop: A Framework for Trustworthy Machine Learning in Power Systems","created":"2022-03-14","cited_by_count":9,"area":null},{"id":"2112.08091","title":"Ensuring DNN Solution Feasibility for Optimization Problems with Convex Constraints and Its Application to DC Optimal Power Flow Problems","created":"2021-12-15","cited_by_count":6,"area":null},{"id":"2601.14517","title":"Learning PDE Solvers with Physics and Data: A Unifying View of Physics-Informed Neural Networks and Neural Operators","created":"2026-01-20","cited_by_count":6,"area":null},{"id":"2401.06820","title":"QCQP-Net: Reliably Learning Feasible Alternating Current Optimal Power Flow Solutions Under Constraints","created":"2024-01-11","cited_by_count":5,"area":null},{"id":"2211.07125","title":"Global Performance Guarantees for Neural Network Models of AC Power Flow","created":"2022-11-14","cited_by_count":4,"area":null},{"id":"2304.00062","title":"A Physics-Informed Machine Learning for Electricity Markets: A NYISO Case Study","created":"2023-03-31","cited_by_count":4,"area":null},{"id":"2306.10617","title":"GPU-Accelerated Verification of Machine Learning Models for Power Systems","created":"2023-06-18","cited_by_count":4,"area":null},{"id":"2502.02446","title":"Towards graph neural networks for provably solving convex optimization problems","created":"2025-02-04","cited_by_count":4,"area":null},{"id":"2212.10930","title":"Minimizing Worst-Case Violations of Neural Networks","created":"2022-12-21","cited_by_count":3,"area":null},{"id":"2301.03766","title":"Optimal Power Flow Based on Physical-Model-Integrated Neural Network with Worth-Learning Data Generation","created":"2023-01-10","cited_by_count":3,"area":null},{"id":"2401.07465","title":"Power Flow Analysis Using Deep Neural Networks in Three-Phase Unbalanced Smart Distribution Grids","created":"2024-01-15","cited_by_count":3,"area":null},{"id":"2404.11606","title":"Learning to Solve the Constrained Most Probable Explanation Task in Probabilistic Graphical Models","created":"2024-04-17","cited_by_count":3,"area":null},{"id":"2409.06649","title":"KANtrol: A Physics-Informed Kolmogorov-Arnold Network Framework for Solving Multi-Dimensional and Fractional Optimal Control Problems","created":"2024-09-10","cited_by_count":3,"area":null},{"id":"2510.04978","title":"Aligning Perception, Reasoning, Modeling and Interaction: A Survey on Physical AI","created":"2025-10-06","cited_by_count":3,"area":"wm"},{"id":"2206.01864","title":"Model-Informed Generative Adversarial Network (MI-GAN) for Learning Optimal Power Flow","created":"2022-06-04","cited_by_count":2,"area":null},{"id":"2305.00216","title":"Physics-Guided Graph Neural Networks for Real-time AC/DC Power Flow Analysis","created":"2023-04-29","cited_by_count":2,"area":null},{"id":"2306.06674","title":"Self-supervised Equality Embedded Deep Lagrange Dual for Approximate Constrained Optimization","created":"2023-06-11","cited_by_count":2,"area":null},{"id":"2506.13777","title":"A Survey of Physics-Informed AI for Complex Urban Systems","created":"2025-06-09","cited_by_count":2,"area":null},{"id":"2605.02133","title":"LUMINA: A Grid Foundation Model for Benchmarking AC Optimal Power Flow Surrogate Learning","created":"2026-05-04","cited_by_count":2,"area":null},{"id":"2205.09452","title":"Learning-based AC-OPF Solvers on Realistic Network and Realistic Loads","created":"2022-05-19","cited_by_count":1,"area":null},{"id":"2206.06789","title":"Grid-SiPhyR: An end-to-end learning to optimize framework for combinatorial problems in power systems","created":"2022-06-11","cited_by_count":1,"area":null},{"id":"2302.00990","title":"Physics Informed Piecewise Linear Neural Networks for Process Optimization","created":"2023-02-02","cited_by_count":1,"area":null},{"id":"2402.07621","title":"Correctness Verification of Neural Networks Approximating Differential Equations","created":"2024-02-12","cited_by_count":1,"area":null},{"id":"2410.04096","title":"Sinc Kolmogorov-Arnold network and its application for solving PDEs with singularities","created":"2024-10-05","cited_by_count":1,"area":null},{"id":"2412.02659","title":"Adaptive Informed Deep Neural Networks for Power Flow Analysis","created":"2024-12-03","cited_by_count":1,"area":null},{"id":"2505.12302","title":"SenseFlow: A Physics-Informed and Self-Ensembling Iterative Framework for Power Flow Estimation","created":"2025-05-18","cited_by_count":1,"area":null},{"id":"2506.05567","title":"Partially-Supervised Neural Network Model For Quadratic Multiparametric Programming","created":"2025-06-05","cited_by_count":1,"area":null},{"id":"2601.09533","title":"Residual Power Flow for Neural Solvers","created":"2026-01-14","cited_by_count":1,"area":null},{"id":"2605.02026","title":"Towards Systematic Generalization for Power Grid Optimization Problems","created":"2026-05-03","cited_by_count":1,"area":null},{"id":"2303.13228","title":"Enriching Neural Network Training Dataset to Improve Worst-Case Performance Guarantees","created":"2023-03-23","cited_by_count":0,"area":null},{"id":"2306.10080","title":"AI Driven Near Real-time Locational Marginal Pricing Method: A Feasibility and Robustness Study","created":"2023-06-16","cited_by_count":0,"area":null},{"id":"2307.15088","title":"Equitable Time-Varying Pricing Tariff Design: A Joint Learning and Optimization Approach","created":"2023-07-26","cited_by_count":0,"area":null},{"id":"2311.12947","title":"PINNs-Based Uncertainty Quantification for Transient Stability Analysis","created":"2023-11-21","cited_by_count":0,"area":null},{"id":"2401.11217","title":"A Hybrid Approach of Transfer Learning and Physics-Informed Modeling: Improving Dissolved Oxygen Concentration Prediction in an Industrial Wastewater Treatment Plant","created":"2024-01-20","cited_by_count":0,"area":null},{"id":"2502.00552","title":"Optimal Sensor Placement in Power Transformers Using Physics-Informed Neural Networks","created":"2025-02-01","cited_by_count":0,"area":null},{"id":"2510.16064","title":"Residual Correction Models for AC Optimal Power Flow Using DC Optimal Power Flow Solutions","created":"2025-10-17","cited_by_count":0,"area":null},{"id":"2510.17380","title":"Optimizing Energy Management of Smart Grid using Reinforcement Learning aided by Surrogate models built using Physics-informed Neural Networks","created":"2025-10-20","cited_by_count":0,"area":"deeprl"},{"id":"2510.23737","title":"A Neural Network Framework for Discovering Closed-form Solutions to Quadratic Programs with Linear Constraints","created":"2025-10-27","cited_by_count":0,"area":null},{"id":"2511.11677","title":"Homotopy-Guided Self-Supervised Learning of Parametric Solutions for AC Optimal Power Flow","created":"2025-11-11","cited_by_count":0,"area":null},{"id":"2512.11127","title":"Refining Graphical Neural Network Predictions Using Flow Matching for Optimal Power Flow with Constraint-Satisfaction Guarantee","created":"2025-12-11","cited_by_count":0,"area":null},{"id":"2602.17975","title":"Generating adversarial inputs for a graph neural network model of AC power flow","created":"2026-02-20","cited_by_count":0,"area":null},{"id":"2605.23194","title":"Scalable Heterogeneous Graph Foundation Models for Data-Driven Optimal Power Flow in Smart Grids","created":"2026-05-22","cited_by_count":0,"area":null},{"id":"2607.22788","title":"FMOPF: Latent Flow Matching with Constraint-Aware Interaction Priors for AC Optimal Power Flow","created":"2026-07-24","cited_by_count":0,"area":null},{"id":"2609.16282","title":"Scaling Laws for Physics-Aware ACOPF Surrogate Learning","created":"2026-09-14","cited_by_count":0,"area":null},{"id":"2610.02014","title":"Atoms to Processes: The Role of Artificial Intelligence and Machine Learning in Chemical Engineering","created":"2026-10-01","cited_by_count":0,"area":null},{"id":"2610.04307","title":"ML-OPF-Bench: Benchmarking Machine Learning for Optimal Power Flow","created":"2026-10-03","cited_by_count":0,"area":null},{"id":"2610.05959","title":"Physics-Informed but Not Physics-Consistent: Error Geometry and Subspace Projection for Neural AC Power Flow","created":"2026-10-05","cited_by_count":0,"area":null}],"next_cursor":null}