{"total":32,"rows":[{"id":"2402.15715","title":"Operator Learning: Algorithms and Analysis","created":"2024-02-24","cited_by_count":85,"area":null},{"id":"2312.14688","title":"A Mathematical Guide to Operator Learning","created":"2023-12-22","cited_by_count":76,"area":null},{"id":"2402.01960","title":"Calibrated Uncertainty Quantification for Operator Learning via Conformal Prediction","created":"2024-02-02","cited_by_count":26,"area":null},{"id":"2504.03503","title":"Operator Learning: A Statistical Perspective","created":"2025-04-04","cited_by_count":17,"area":null},{"id":"2403.10642","title":"Using Uncertainty Quantification to Characterize and Improve Out-of-Domain Learning for PDEs","created":"2024-03-15","cited_by_count":15,"area":null},{"id":"2401.01783","title":"Approximating Numerical Fluxes Using Fourier Neural Operators for Hyperbolic Conservation Laws","created":"2024-01-03","cited_by_count":11,"area":null},{"id":"2406.18794","title":"Operator Learning of Lipschitz Operators: An Information-Theoretic Perspective","created":"2024-06-26","cited_by_count":11,"area":null},{"id":"2501.06933","title":"Neural equilibria for long-term prediction of nonlinear conservation laws","created":"2025-01-12","cited_by_count":10,"area":null},{"id":"2510.25379","title":"A Deep Learning Framework for Multi-Operator Learning: Architectures and Approximation Theory","created":"2025-10-29","cited_by_count":9,"area":null},{"id":"2405.12380","title":"Fast meta-solvers for 3D complex-shape scatterers using neural operators trained on a non-scattering problem","created":"2024-05-20","cited_by_count":8,"area":null},{"id":"2508.14995","title":"Generative Neural Operators of Log-Complexity Can Simultaneously Solve Infinitely Many Convex Programs","created":"2025-08-20","cited_by_count":7,"area":null},{"id":"2408.13885","title":"Neural Spacetimes for DAG Representation Learning","created":"2024-08-25","cited_by_count":5,"area":null},{"id":"2411.09644","title":"Neural Operators Can Play Dynamic Stackelberg Games","created":"2024-11-14","cited_by_count":5,"area":null},{"id":"2509.04623","title":"Split Conformal Prediction in the Function Space with Neural Operators","created":"2025-09-04","cited_by_count":4,"area":null},{"id":"2605.22724","title":"Multiple Neural Operators Achieve Near-Optimal Rates for Multi-Task Learning","created":"2026-05-21","cited_by_count":4,"area":null},{"id":"2505.21626","title":"Learning where to learn: Training data distribution optimization for scientific machine learning","created":"2025-05-27","cited_by_count":3,"area":null},{"id":"2508.20650","title":"Self-composing neural operators for high-frequency and multiscale PDE surrogates","created":"2025-08-28","cited_by_count":3,"area":null},{"id":"2510.20017","title":"Simultaneously Solving Infinitely Many LQ Mean Field Games In Hilbert Spaces: The Power of Neural Operators","created":"2025-10-22","cited_by_count":3,"area":null},{"id":"2604.01961","title":"Generalization Bounds and Statistical Guarantees for Multi-Task and Multiple Operator Learning with MNO Networks","created":"2026-04-02","cited_by_count":3,"area":null},{"id":"2404.16015","title":"Neural Operators Learn the Local Physics of Magnetohydrodynamics","created":"2024-04-24","cited_by_count":2,"area":null},{"id":"2410.14788","title":"Polynomial Scaling is Possible For Neural Operator Approximations of Structured Families of BSDEs","created":"2024-10-18","cited_by_count":2,"area":null},{"id":"2507.20975","title":"Locally Adaptive Conformal Inference for Operator Models","created":"2025-07-28","cited_by_count":2,"area":null},{"id":"2509.20591","title":"Learning Greens Operators through Hierarchical Neural Networks Inspired by the Fast Multipole Method","created":"2025-09-24","cited_by_count":2,"area":null},{"id":"2602.15184","title":"Learning Data-Efficient and Generalizable Neural Operators via Fundamental Physics Knowledge","created":"2026-02-16","cited_by_count":2,"area":null},{"id":"2602.11197","title":"Hybrid operator learning of wave scattering maps in high-contrast media","created":"2026-01-30","cited_by_count":1,"area":null},{"id":"2603.00971","title":"Random Features for Operator-Valued Kernels: Bridging Kernel Methods and Neural Operators","created":"2026-03-01","cited_by_count":1,"area":null},{"id":"2507.21833","title":"Analysis of Fourier Neural Operators via Effective Field Theory","created":"2025-07-29","cited_by_count":0,"area":null},{"id":"2602.23528","title":"Neural Operators Can Discover Functional Clusters","created":"2026-02-26","cited_by_count":0,"area":null},{"id":"2605.10277","title":"Generalization Error Bounds for Picard-Type Operator Learning in Nonlinear Parabolic PDEs","created":"2026-05-11","cited_by_count":0,"area":null},{"id":"2605.26732","title":"APEX: Amplitude Anchors and Phase Priors for Target-Scarce Higher-Frequency Wave Prediction","created":"2026-05-26","cited_by_count":0,"area":null},{"id":"2608.24049","title":"Physics-Integrated Operator Learning via Gaussian Splatting Representations","created":"2026-08-25","cited_by_count":0,"area":null},{"id":"2609.33978","title":"Adapting neural operators for mechanics decisions under changing operating conditions","created":"2026-09-27","cited_by_count":0,"area":null}],"next_cursor":null}