{"total":162,"rows":[{"id":"2109.01050","title":"Characterizing possible failure modes in physics-informed neural networks","created":"2021-09-02","cited_by_count":643,"area":null},{"id":"2202.00665","title":"Tutorial on amortized optimization","created":"2022-02-01","cited_by_count":83,"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":"2307.01717","title":"On the Constrained Time-Series Generation Problem","created":"2023-07-04","cited_by_count":54,"area":null},{"id":"2110.15767","title":"Adversarial Robustness with Semi-Infinite Constrained Learning","created":"2021-10-29","cited_by_count":39,"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":"2207.08675","title":"Learning differentiable solvers for systems with hard constraints","created":"2022-07-18","cited_by_count":34,"area":null},{"id":"2208.05424","title":"Hard-Constrained Deep Learning for Climate Downscaling","created":"2022-08-08","cited_by_count":32,"area":null},{"id":"2410.10807","title":"HardNet: Hard-Constrained Neural Networks with Universal Approximation Guarantees","created":"2024-10-14","cited_by_count":31,"area":null},{"id":"2307.08336","title":"RAYEN: Imposition of Hard Convex Constraints on Neural Networks","created":"2023-07-17","cited_by_count":30,"area":null},{"id":"2112.09161","title":"Constraint-based graph network simulator","created":"2021-12-16","cited_by_count":27,"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":"2212.05727","title":"Evaluating Model-free Reinforcement Learning toward Safety-critical Tasks","created":"2022-12-12","cited_by_count":22,"area":null},{"id":"2210.12547","title":"SurCo: Learning Linear Surrogates For Combinatorial Nonlinear Optimization Problems","created":"2022-10-22","cited_by_count":21,"area":null},{"id":"2212.07477","title":"Guiding continuous operator learning through Physics-based boundary constraints","created":"2022-12-14","cited_by_count":21,"area":null},{"id":"2506.00362","title":"FSNet: Feasibility-Seeking Neural Network for Constrained Optimization with Guarantees","created":"2025-05-31","cited_by_count":21,"area":null},{"id":"2204.11144","title":"Competitive Physics Informed Networks","created":"2022-04-23","cited_by_count":19,"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":"2502.06774","title":"ENFORCE: Nonlinear Constrained Learning with Adaptive-depth Neural Projection","created":"2025-02-10","cited_by_count":17,"area":null},{"id":"2403.03454","title":"Learning Constrained Optimization with Deep Augmented Lagrangian Methods","created":"2024-03-06","cited_by_count":16,"area":null},{"id":"2311.00277","title":"OpenForest: A data catalogue for machine learning in forest monitoring","created":"2023-11-01","cited_by_count":15,"area":null},{"id":"2507.08124","title":"Physics-Informed Neural Networks with Hard Nonlinear Equality and Inequality Constraints","created":"2025-07-10","cited_by_count":14,"area":null},{"id":"2207.11417","title":"Multiscale Neural Operator: Learning Fast and Grid-independent PDE Solvers","created":"2022-07-23","cited_by_count":12,"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":"2307.10459","title":"A New Computationally Simple Approach for Implementing Neural Networks with Output Hard Constraints","created":"2023-07-19","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":"2505.22825","title":"PGLearn -- An Open-Source Learning Toolkit for Optimal Power Flow","created":"2025-05-28","cited_by_count":11,"area":null},{"id":"2107.10254","title":"Neural Fixed-Point Acceleration for Convex Optimization","created":"2021-07-21","cited_by_count":10,"area":null},{"id":"2311.15341","title":"Generative Modelling of Stochastic Actions with Arbitrary Constraints in Reinforcement Learning","created":"2023-11-26","cited_by_count":10,"area":null},{"id":"2403.00329","title":"Learning with Logical Constraints but without Shortcut Satisfaction","created":"2024-03-01","cited_by_count":10,"area":null},{"id":"2411.19285","title":"BPQP: A Differentiable Convex Optimization Framework for Efficient End-to-End Learning","created":"2024-11-28","cited_by_count":10,"area":null},{"id":"2505.13858","title":"Enforcing Hard Linear Constraints in Deep Learning Models with Decision Rules","created":"2025-05-20","cited_by_count":10,"area":null},{"id":"2207.11786","title":"Physics-Informed Learning of Aerosol Microphysics","created":"2022-07-24","cited_by_count":9,"area":null},{"id":"2312.01811","title":"Energy-based Potential Games for Joint Motion Forecasting and Control","created":"2023-12-04","cited_by_count":9,"area":null},{"id":"2502.02287","title":"Adaptive Resource Allocation Optimization Using Large Language Models in Dynamic Wireless Environments","created":"2025-02-04","cited_by_count":9,"area":null},{"id":"2506.07003","title":"End-to-End Probabilistic Framework for Learning with Hard Constraints","created":"2025-06-08","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":"2112.02441","title":"DNN-based Policies for Stochastic AC OPF","created":"2021-12-04","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":"2310.09574","title":"Reduced Policy Optimization for Continuous Control with Hard Constraints","created":"2023-10-14","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":"2608.09921","title":"GENCO - A Unified Neural Solver Embedded in a Development Framework for Steady-State Grid Analysis","created":"2026-08-10","cited_by_count":6,"area":null},{"id":"2110.01653","title":"Learning to Solve the AC Optimal Power Flow via a Lagrangian Approach","created":"2021-10-04","cited_by_count":5,"area":null},{"id":"2306.08943","title":"Neural Fields with Hard Constraints of Arbitrary Differential Order","created":"2023-06-15","cited_by_count":5,"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":"2404.13831","title":"Data-Driven Performance Guarantees for Classical and Learned Optimizers","created":"2024-04-22","cited_by_count":5,"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":"2304.07151","title":"End-to-End Learning with Multiple Modalities for System-Optimised Renewables Nowcasting","created":"2023-04-14","cited_by_count":4,"area":null},{"id":"2308.16539","title":"On a Connection between Differential Games, Optimal Control, and Energy-based Models for Multi-Agent Interactions","created":"2023-08-31","cited_by_count":4,"area":null},{"id":"2311.06972","title":"An Expandable Machine Learning-Optimization Framework to Sequential Decision-Making","created":"2023-11-12","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":"2402.02596","title":"Dual Interior Point Optimization Learning","created":"2024-02-04","cited_by_count":4,"area":null},{"id":"2409.08066","title":"Self-Supervised Learning of Iterative Solvers for Constrained Optimization","created":"2024-09-12","cited_by_count":4,"area":null},{"id":"2409.18946","title":"Unconditional stability of a recurrent neural circuit implementing divisive normalization","created":"2024-09-27","cited_by_count":4,"area":null},{"id":"2410.11284","title":"Calabi-Yau metrics through Grassmannian learning and Donaldson's algorithm","created":"2024-10-15","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":"2510.24039","title":"Geometric Algorithms for Neural Combinatorial Optimization with Constraints","created":"2025-10-28","cited_by_count":4,"area":null},{"id":"2202.11246","title":"Learning Neural Networks under Input-Output Specifications","created":"2022-02-23","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":"2310.02442","title":"GenCO: Generating Diverse Designs with Combinatorial Constraints","created":"2023-10-03","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":"2409.04898","title":"Learning Joint Models of Prediction and Optimization","created":"2024-09-07","cited_by_count":3,"area":null},{"id":"2410.01786","title":"Learning To Solve Differential Equation Constrained Optimization Problems","created":"2024-10-02","cited_by_count":3,"area":null},{"id":"2505.11360","title":"Efficient End-to-End Learning for Decision-Making: A Meta-Optimization Approach","created":"2025-05-16","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":"2512.05881","title":"DAE-HardNet: A Physics Constrained Neural Network Enforcing Differential-Algebraic Hard Constraints","created":"2025-12-05","cited_by_count":3,"area":null},{"id":"2602.03461","title":"Soft-Radial Projection for Constrained End-to-End Learning","created":"2026-02-03","cited_by_count":3,"area":null},{"id":"2604.03489","title":"Improving Feasibility via Fast Autoencoder-Based Projections","created":"2026-04-03","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":"2306.01787","title":"Power Control with QoS Guarantees: A Differentiable Projection-based Unsupervised Learning Framework","created":"2023-05-31","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":"2308.10806","title":"DFWLayer: Differentiable Frank-Wolfe Optimization Layer","created":"2023-08-21","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":"2505.11342","title":"Sobolev Training of End-to-End Optimization Proxies","created":"2025-05-16","cited_by_count":2,"area":null},{"id":"2602.09317","title":"SnareNet: Flexible Repair Layers for Neural Networks with Hard Constraints","created":"2026-02-10","cited_by_count":2,"area":null},{"id":"2604.19669","title":"HardNet++: Nonlinear Constraint Enforcement in Neural Networks","created":"2026-04-21","cited_by_count":2,"area":null},{"id":"2109.13361","title":"Graph Neural Network-based Resource Allocation Strategies for Multi-Object Spectroscopy","created":"2021-09-27","cited_by_count":1,"area":null},{"id":"2205.03673","title":"Towards Practical Physics-Informed ML Design and Evaluation for Power Grid","created":"2022-05-07","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":"2212.11805","title":"BSAC-CoEx: Coexistence of URLLC and Distributed Learning Services via Device Selection","created":"2022-12-22","cited_by_count":1,"area":null},{"id":"2302.04729","title":"Constrained Empirical Risk Minimization: Theory and Practice","created":"2023-02-09","cited_by_count":1,"area":null},{"id":"2304.10702","title":"Power Grid Behavioral Patterns and Risks of Generalization in Applied Machine Learning","created":"2023-04-21","cited_by_count":1,"area":null},{"id":"2311.01568","title":"Anytime-Competitive Reinforcement Learning with Policy Prior","created":"2023-11-02","cited_by_count":1,"area":null},{"id":"2406.07785","title":"From Variance to Veracity: Unbundling and Mitigating Gradient Variance in Differentiable Bundle Adjustment Layers","created":"2024-06-12","cited_by_count":1,"area":null},{"id":"2409.10499","title":"Partial Distribution Matching via Partial Wasserstein Adversarial Networks","created":"2024-09-16","cited_by_count":1,"area":null},{"id":"2410.04818","title":"Physics-Informed Graph Neural Networks for Robust AC-Optimal Power Flow","created":"2024-10-07","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":"2502.00304","title":"HoP: Homeomorphic Polar Learning for Hard Constrained Optimization","created":"2025-02-01","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":"2503.10048","title":"Model-Agnostic Knowledge Guided Correction for Improved Neural Surrogate Rollout","created":"2025-03-13","cited_by_count":1,"area":null},{"id":"2505.03779","title":"Neural Co-Optimization of Structural Topology, Manufacturable Layers, and Path Orientations for Fiber-Reinforced Composites","created":"2025-04-30","cited_by_count":1,"area":null},{"id":"2509.00655","title":"Revisiting Deep AC-OPF","created":"2025-08-31","cited_by_count":1,"area":null},{"id":"2509.24573","title":"Learning to Solve Optimization Problems Constrained with Partial Differential Equations","created":"2025-09-29","cited_by_count":1,"area":null}],"next_cursor":"100"}