{"total":21,"rows":[{"id":"2505.02308","title":"Enabling Local Neural Operators to perform Equation-Free System-Level Analysis","created":"2025-05-05","cited_by_count":10,"area":null},{"id":"2508.20886","title":"Polynomial Chaos Expansion for Operator Learning","created":"2025-08-28","cited_by_count":8,"area":null},{"id":"2512.09165","title":"Spectral Embedding via Chebyshev Bases for Robust DeepONet Approximation","created":"2025-12-09","cited_by_count":6,"area":null},{"id":"2510.23810","title":"A Physics-informed Multi-resolution Neural Operator","created":"2025-10-27","cited_by_count":4,"area":null},{"id":"2508.19847","title":"Physics-Informed DeepONet Coupled with FEM for Convective Transport in Porous Media with Sharp Gaussian Sources","created":"2025-08-27","cited_by_count":3,"area":null},{"id":"2511.12890","title":"Method of Manufactured Learning for Solver-free Training of Neural Operators","created":"2025-11-17","cited_by_count":3,"area":null},{"id":"2507.12814","title":"RONOM: Reduced-Order Neural Operator Modeling","created":"2025-07-17","cited_by_count":2,"area":null},{"id":"2509.11966","title":"Deep operator network for surrogate modeling of poroelasticity with random permeability fields","created":"2025-09-15","cited_by_count":1,"area":null},{"id":"2510.03248","title":"Multimodal Neural Operators for Real-Time Biomechanical Modelling of Traumatic Brain Injury","created":"2025-09-26","cited_by_count":1,"area":null},{"id":"2510.16816","title":"Efficient High-Accuracy PDEs Solver with the Linear Attention Neural Operator","created":"2025-10-19","cited_by_count":1,"area":null},{"id":"2510.20970","title":"On the accuracy of implicit neural representations for cardiovascular anatomies and hemodynamic fields","created":"2025-10-23","cited_by_count":1,"area":null},{"id":"2602.24228","title":"BLISSNet: Deep Operator Learning for Fast and Accurate Flow Reconstruction from Sparse Sensor Measurements","created":"2026-02-27","cited_by_count":1,"area":null},{"id":"2503.23236","title":"UP-dROM : Uncertainty-Aware and Parametrised dynamic Reduced-Order Model, application to unsteady flows","created":"2025-03-29","cited_by_count":0,"area":null},{"id":"2506.15199","title":"Interpretability and Generalization Bounds for Learning Spatial Physics","created":"2025-06-18","cited_by_count":0,"area":null},{"id":"2510.10350","title":"Prescribed-Basis Coefficient-to-Coefficient Neural Operator for Partial Differential Equations","created":"2025-10-11","cited_by_count":0,"area":null},{"id":"2512.18120","title":"Learning Generalizable Neural Operators for Inverse Problems","created":"2025-12-19","cited_by_count":0,"area":null},{"id":"2603.26023","title":"GLU: Global-Local-Uncertainty Fusion for Scalable Spatiotemporal Reconstruction and Forecasting","created":"2026-03-27","cited_by_count":0,"area":null},{"id":"2605.12700","title":"UFO: A Domain-Unification-Free Operator Framework for Generalized Operator Learning","created":"2026-05-12","cited_by_count":0,"area":null},{"id":"2605.28551","title":"Resolution-free neural surrogates for geometric parameterization and mapping with spatially varying fields","created":"2026-05-27","cited_by_count":0,"area":null},{"id":"2608.29069","title":"Spectral-Embedded Operator Learning for Three-Phase Interfacial Flow: A Ternary Cahn-Hilliard-Navier-Stokes Benchmark","created":"2026-08-29","cited_by_count":0,"area":null},{"id":"2609.35938","title":"KernelOnet: An Interpretable Neural Operator Based on Kernel Functions","created":"2026-09-28","cited_by_count":0,"area":null}],"next_cursor":null}