{"total":20,"rows":[{"id":"2102.04626","title":"Physics-informed neural networks with hard constraints for inverse design","created":"2021-02-09","cited_by_count":280,"area":null},{"id":"2109.06407","title":"Neural Networks with Physics-Informed Architectures and Constraints for Dynamical Systems Modeling","created":"2021-09-14","cited_by_count":41,"area":null},{"id":"2109.14860","title":"Physics and Equality Constrained Artificial Neural Networks: Application to Forward and Inverse Problems with Multi-fidelity Data Fusion","created":"2021-09-30","cited_by_count":38,"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":"2402.07251","title":"Physics-Informed Neural Networks with Hard Linear Equality Constraints","created":"2024-02-11","cited_by_count":30,"area":null},{"id":"2306.04904","title":"An adaptive augmented Lagrangian method for training physics and equality constrained artificial neural networks","created":"2023-06-08","cited_by_count":16,"area":null},{"id":"2311.00678","title":"Complexity of Single Loop Algorithms for Nonlinear Programming with Stochastic Objective and Constraints","created":"2023-11-01","cited_by_count":16,"area":null},{"id":"2210.00973","title":"NCVX: A General-Purpose Optimization Solver for Constrained Machine and Deep Learning","created":"2022-10-03","cited_by_count":7,"area":null},{"id":"2303.16110","title":"Invariant preservation in machine learned PDE solvers via error correction","created":"2023-03-28","cited_by_count":7,"area":null},{"id":"2212.01346","title":"Guaranteed Conformance of Neurosymbolic Models to Natural Constraints","created":"2022-12-02","cited_by_count":6,"area":null},{"id":"2504.07607","title":"Stochastic Smoothed Primal-Dual Algorithms for Nonconvex Optimization with Linear Inequality Constraints","created":"2025-04-10","cited_by_count":5,"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":"2409.10777","title":"Physics-Informed Neural Networks with Trust-Region Sequential Quadratic Programming","created":"2024-09-16","cited_by_count":4,"area":null},{"id":"2504.05248","title":"PINNverse: Accurate parameter estimation in differential equations from noisy data with constrained physics-informed neural networks","created":"2025-04-07","cited_by_count":4,"area":null},{"id":"2508.15695","title":"Conditionally adaptive augmented Lagrangian method for physics-informed learning of forward and inverse problems","created":"2025-08-21","cited_by_count":3,"area":null},{"id":"2111.08626","title":"Adjoint-Matching Neural Network Surrogates for Fast 4D-Var Data Assimilation","created":"2021-11-16","cited_by_count":2,"area":null},{"id":"2204.12972","title":"Multi-Objective Physics-Guided Recurrent Neural Networks for Identifying Non-Autonomous Dynamical Systems","created":"2022-04-27","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":"2601.16812","title":"Sample-wise Constrained Learning via a Sequential Penalty Approach with Applications in Image Processing","created":"2026-01-23","cited_by_count":0,"area":null},{"id":"2610.09040","title":"ORACLE: Optimizer-Relative Alignment for Constrained LEarning","created":"2026-10-06","cited_by_count":0,"area":null}],"next_cursor":null}