{"total":25,"rows":[{"id":"2110.02672","title":"Physics-Informed Neural Networks for AC Optimal Power Flow","created":"2021-10-06","cited_by_count":54,"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":"2501.00200","title":"Scalable Neural Network Verification with Branch-and-bound Inferred Cutting Planes","created":"2024-12-31","cited_by_count":33,"area":null},{"id":"2110.02121","title":"Optimization with Constraint Learning: A Framework and Survey","created":"2021-10-05","cited_by_count":28,"area":null},{"id":"2107.00465","title":"Physics-Informed Neural Networks for Minimising Worst-Case Violations in DC Optimal Power Flow","created":"2021-06-28","cited_by_count":23,"area":null},{"id":"2110.11269","title":"Modeling the AC Power Flow Equations with Optimally Compact Neural Networks: Application to Unit Commitment","created":"2021-10-21","cited_by_count":17,"area":null},{"id":"2101.06768","title":"Spatial Network Decomposition for Fast and Scalable AC-OPF Learning","created":"2021-01-17","cited_by_count":15,"area":null},{"id":"2211.15755","title":"Confidence-Aware Graph Neural Networks for Learning Reliability Assessment Commitments","created":"2022-11-28","cited_by_count":12,"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":"2512.11087","title":"Clip-and-Verify: Linear Constraint-Driven Domain Clipping for Accelerating Neural Network Verification","created":"2025-12-11","cited_by_count":9,"area":null},{"id":"2205.03787","title":"Learning Regionally Decentralized AC Optimal Power Flows with ADMM","created":"2022-05-08","cited_by_count":7,"area":null},{"id":"2106.05997","title":"Verifying Quantized Neural Networks using SMT-Based Model Checking","created":"2021-06-10","cited_by_count":6,"area":null},{"id":"2110.11382","title":"Efficient and Robust Mixed-Integer Optimization Methods for Training Binarized Deep Neural Networks","created":"2021-10-21","cited_by_count":4,"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":"2306.10617","title":"GPU-Accelerated Verification of Machine Learning Models for Power Systems","created":"2023-06-18","cited_by_count":4,"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":"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":"2311.06973","title":"Analytical Verification of Performance of Deep Neural Network Based Time-Synchronized Distribution System State Estimation","created":"2023-11-12","cited_by_count":1,"area":null},{"id":"2410.21712","title":"Sliced-Wasserstein-based Anomaly Detection and Open Dataset for Localized Critical Peak Rebates","created":"2024-10-29","cited_by_count":1,"area":null},{"id":"2202.07421","title":"Adversarial Attacks and Defense Methods for Power Quality Recognition","created":"2022-02-11","cited_by_count":0,"area":null},{"id":"2408.01528","title":"Can multivariate Granger causality detect directed connectivity of a multistable and dynamic biological decision network model?","created":"2024-08-02","cited_by_count":0,"area":null},{"id":"2504.12918","title":"Sliced-Wasserstein Distance-based Data Selection","created":"2025-04-17","cited_by_count":0,"area":null},{"id":"2504.13314","title":"On the Definition of Robustness and Resilience of AI Agents for Real-time Congestion Management","created":"2025-04-17","cited_by_count":0,"area":null},{"id":"2601.13303","title":"On the Extreme Variance of Certified Local Robustness Across Model Seeds","created":"2026-01-19","cited_by_count":0,"area":null},{"id":"2606.19652","title":"Convex training of Lipschitz-regularized shallow neural networks","created":"2026-06-17","cited_by_count":0,"area":null}],"next_cursor":null}