{"total":89,"rows":[{"id":"2103.16378","title":"End-to-End Constrained Optimization Learning: A Survey","created":"2021-03-30","cited_by_count":157,"area":null},{"id":"2208.09046","title":"Self-Supervised Primal-Dual Learning for Constrained Optimization","created":"2022-08-18","cited_by_count":61,"area":null},{"id":"2110.02672","title":"Physics-Informed Neural Networks for AC Optimal Power Flow","created":"2021-10-06","cited_by_count":54,"area":null},{"id":"2106.02875","title":"Integrating Expert ODEs into Neural ODEs: Pharmacology and Disease Progression","created":"2021-06-05","cited_by_count":48,"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":"2105.08881","title":"Enforcing Policy Feasibility Constraints through Differentiable Projection for Energy Optimization","created":"2021-05-19","cited_by_count":31,"area":null},{"id":"2112.13469","title":"Learning Optimization Proxies for Large-Scale Security-Constrained Economic Dispatch","created":"2021-12-27","cited_by_count":29,"area":null},{"id":"2403.17660","title":"CANOS: A Fast and Scalable Neural AC-OPF Solver Robust To N-1 Perturbations","created":"2024-03-26","cited_by_count":27,"area":null},{"id":"2103.14779","title":"Learning to Solve the AC-OPF using Sensitivity-Informed Deep Neural Networks","created":"2021-03-27","cited_by_count":26,"area":null},{"id":"2406.07234","title":"OPFData: Large-scale datasets for AC optimal power flow with topological perturbations","created":"2024-06-11","cited_by_count":24,"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":"2110.00306","title":"Leveraging power grid topology in machine learning assisted optimal power flow","created":"2021-10-01","cited_by_count":22,"area":null},{"id":"2106.02601","title":"Learning Hard Optimization Problems: A Data Generation Perspective","created":"2021-06-04","cited_by_count":21,"area":null},{"id":"2110.06365","title":"Fast Approximations for Job Shop Scheduling: A Lagrangian Dual Deep Learning Method","created":"2021-10-12","cited_by_count":20,"area":null},{"id":"2111.01228","title":"OPF-Learn: An Open-Source Framework for Creating Representative AC Optimal Power Flow Datasets","created":"2021-11-01","cited_by_count":18,"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":"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":"2402.03086","title":"Dual Lagrangian Learning for Conic Optimization","created":"2024-02-05","cited_by_count":11,"area":null},{"id":"2110.04600","title":"A Review of Physics-based Machine Learning in Civil Engineering","created":"2021-10-09","cited_by_count":9,"area":null},{"id":"2410.11061","title":"Learning to Optimize for Mixed-Integer Non-linear Programming with Feasibility Guarantees","created":"2024-10-14","cited_by_count":8,"area":null},{"id":"2603.04300","title":"LUMINA: Foundation Models for Topology Transferable ACOPF","created":"2026-03-04","cited_by_count":8,"area":null},{"id":"2110.13772","title":"Data-Driven Time Series Reconstruction for Modern Power Systems Research","created":"2021-10-26","cited_by_count":7,"area":null},{"id":"2204.00950","title":"Risk-Aware Control and Optimization for High-Renewable Power Grids","created":"2022-04-02","cited_by_count":7,"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":"2310.04605","title":"Learning Optimal Power Flow Value Functions with Input-Convex Neural Networks","created":"2023-10-06","cited_by_count":7,"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":"2206.10579","title":"Gradient-Enhanced Physics-Informed Neural Networks for Power Systems Operational Support","created":"2022-06-21","cited_by_count":6,"area":null},{"id":"2404.00882","title":"Metric Learning to Accelerate Convergence of Operator Splitting Methods for Differentiable Parametric Programming","created":"2024-04-01","cited_by_count":6,"area":null},{"id":"2510.06860","title":"Towards Generalization of Graph Neural Networks for AC Optimal Power Flow","created":"2025-10-08","cited_by_count":6,"area":null},{"id":"2301.09703","title":"Two-Stage Learning For the Flexible Job Shop Scheduling Problem","created":"2023-01-23","cited_by_count":4,"area":null},{"id":"2308.12161","title":"Data-driven decision-focused surrogate modeling","created":"2023-08-23","cited_by_count":4,"area":null},{"id":"2310.16647","title":"Achieving Constraints in Neural Networks: A Stochastic Augmented Lagrangian Approach","created":"2023-10-25","cited_by_count":4,"area":null},{"id":"2311.13087","title":"Predict-Then-Optimize by Proxy: Learning Joint Models of Prediction and Optimization","created":"2023-11-22","cited_by_count":4,"area":null},{"id":"2312.05551","title":"Multi-dimensional Fair Federated Learning","created":"2023-12-09","cited_by_count":4,"area":null},{"id":"2402.02596","title":"Dual Interior Point Optimization Learning","created":"2024-02-04","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":"2208.07497","title":"Bucketized Active Sampling for Learning ACOPF","created":"2022-08-16","cited_by_count":3,"area":null},{"id":"2210.03324","title":"AutoML for Climate Change: A Call to Action","created":"2022-10-07","cited_by_count":3,"area":null},{"id":"2212.03977","title":"Unsupervised Deep Learning for AC Optimal Power Flow via Lagrangian Duality","created":"2022-12-07","cited_by_count":3,"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":"2307.04050","title":"Optimization-based Learning for Dynamic Load Planning in Trucking Service Networks","created":"2023-07-08","cited_by_count":3,"area":null},{"id":"2311.18072","title":"Self-Supervised Learning for Large-Scale Preventive Security Constrained DC Optimal Power Flow","created":"2023-11-29","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":"2508.01951","title":"Flow-Aware GNN for Transmission Network Reconfiguration via Substation Breaker Optimization","created":"2025-08-03","cited_by_count":3,"area":null},{"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":"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":"2312.03886","title":"On The Fairness Impacts of Hardware Selection in Machine Learning","created":"2023-12-06","cited_by_count":2,"area":null},{"id":"2402.03621","title":"Neural Network Approximators for Marginal MAP in Probabilistic Circuits","created":"2024-02-06","cited_by_count":2,"area":null},{"id":"2405.21023","title":"Compact Optimality Verification for Optimization Proxies","created":"2024-05-31","cited_by_count":2,"area":null},{"id":"2410.03085","title":"Optimization Proxies using Limited Labeled Data and Training Time -- A Semi-Supervised Bayesian Neural Network Approach","created":"2024-10-04","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":"2510.14075","title":"DiffOPF: Diffusion Solver for Optimal Power Flow","created":"2025-10-15","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":"2112.11461","title":"Deep Reinforcement Learning for Optimal Power Flow with Renewables Using Graph Information","created":"2021-12-22","cited_by_count":1,"area":null},{"id":"2206.03365","title":"DeepOPF-AL: Augmented Learning for Solving AC-OPF Problems with Multiple Load-Solution Mappings","created":"2022-06-07","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":"2311.13949","title":"Optimal Power Flow in Highly Renewable Power System Based on Attention Neural Networks","created":"2023-11-23","cited_by_count":1,"area":null},{"id":"2405.10389","title":"Physics-Informed Heterogeneous Graph Neural Networks for DC Blocker Placement","created":"2024-05-16","cited_by_count":1,"area":null},{"id":"2502.06978","title":"Dual Conic Proxy for Semidefinite Relaxation of AC Optimal Power Flow","created":"2025-02-10","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":"2601.02706","title":"Scaling Laws of Machine Learning for Optimal Power Flow","created":"2026-01-06","cited_by_count":1,"area":null},{"id":"2605.07113","title":"Solving Max-Cut to Global Optimality via Feasibility-Preserving Graph Neural Networks","created":"2026-05-08","cited_by_count":1,"area":null},{"id":"2101.03973","title":"Load Encoding for Learning AC-OPF","created":"2021-01-11","cited_by_count":0,"area":null},{"id":"2209.12731","title":"Machine Learning for Improved Gas Network Models in Coordinated Energy Systems","created":"2022-09-26","cited_by_count":0,"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":"2305.11195","title":"DClEVerNet: Deep Combinatorial Learning for Efficient EV Charging Scheduling in Large-scale Networked Facilities","created":"2023-05-18","cited_by_count":0,"area":null},{"id":"2310.08601","title":"Unit Commitment Predictor With a Performance Guarantee: A Support Vector Machine Classifier","created":"2023-10-07","cited_by_count":0,"area":null},{"id":"2311.04838","title":"Toward Rapid, Optimal, and Feasible Power Dispatch through Generalized Neural Mapping","created":"2023-11-08","cited_by_count":0,"area":null},{"id":"2312.03413","title":"Approximating Solutions to the Knapsack Problem using the Lagrangian Dual Framework","created":"2023-12-06","cited_by_count":0,"area":null},{"id":"2405.01200","title":"Learning-to-solve unit commitment based on few-shot physics-guided spatial-temporal graph convolution network","created":"2024-05-02","cited_by_count":0,"area":null},{"id":"2405.09212","title":"SOMTP: Self-Supervised Learning-Based Optimizer for MPC-Based Safe Trajectory Planning Problems in Robotics","created":"2024-05-15","cited_by_count":0,"area":null},{"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":"2512.01219","title":"Neural Network Optimal Power Flow via Energy Gradient Flow and Unified Dynamics","created":"2025-12-01","cited_by_count":0,"area":null},{"id":"2602.10888","title":"Anomaly Detection with Machine Learning Algorithms in Large-Scale Power Grids","created":"2026-02-11","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":"2604.11507","title":"Deep Learning for Sequential Decision Making under Uncertainty: Foundations, Frameworks, and Frontiers","created":"2026-04-13","cited_by_count":0,"area":null},{"id":"2605.05728","title":"WARP: A Benchmark for Primal-Dual Warm-Starting of Interior-Point Solvers","created":"2026-05-07","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":"2606.03125","title":"Rethinking Neural Width for Alternating Current Optimal Power Flow Proxies","created":"2026-06-02","cited_by_count":0,"area":null},{"id":"2606.21440","title":"Fusing Backdoors, Machine Learning, and Optimization for Large-Scale Parametric Mixed-Integer Programs","created":"2026-06-19","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":"2609.30173","title":"GridSFM: A Foundation Model for Solving AC Optimal Power Flow","created":"2026-09-24","cited_by_count":0,"area":null},{"id":"2609.36310","title":"Learning Samples Importance: Parameterizing Dual Variables in Everywhere Learning","created":"2026-09-28","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}],"next_cursor":null}