{"total":42,"rows":[{"id":"2501.13350","title":"DoMINO: A Decomposable Multi-scale Iterative Neural Operator for Modeling Large Scale Engineering Simulations","created":"2025-01-23","cited_by_count":27,"area":null},{"id":"2407.19916","title":"Aero-Nef: Neural Fields for Rapid Aircraft Aerodynamics Simulations","created":"2024-07-29","cited_by_count":24,"area":null},{"id":"2409.18032","title":"FlowBench: A Large Scale Benchmark for Flow Simulation over Complex Geometries","created":"2024-09-26","cited_by_count":19,"area":null},{"id":"2408.02198","title":"Synergistic Learning with Multi-Task DeepONet for Efficient PDE Problem Solving","created":"2024-08-05","cited_by_count":17,"area":null},{"id":"2410.00171","title":"Basis-to-Basis Operator Learning Using Function Encoders","created":"2024-09-30","cited_by_count":17,"area":null},{"id":"2407.15887","title":"Separable DeepONet: Breaking the Curse of Dimensionality in Physics-Informed Machine Learning","created":"2024-07-21","cited_by_count":15,"area":null},{"id":"2502.09346","title":"Machine learning for modelling unstructured grid data in computational physics: a review","created":"2025-02-13","cited_by_count":12,"area":null},{"id":"2412.18362","title":"Point-DeepONet: Predicting Nonlinear Fields on Non-Parametric Geometries under Variable Load Conditions","created":"2024-12-24","cited_by_count":10,"area":null},{"id":"2504.19452","title":"Geometry-Informed Neural Operator Transformer","created":"2025-04-28","cited_by_count":8,"area":null},{"id":"2412.09009","title":"A physics-informed transformer neural operator for learning generalized solutions of initial boundary value problems","created":"2024-12-12","cited_by_count":6,"area":null},{"id":"2601.14517","title":"Learning PDE Solvers with Physics and Data: A Unifying View of Physics-Informed Neural Networks and Neural Operators","created":"2026-01-20","cited_by_count":6,"area":null},{"id":"2504.02459","title":"A Physics-Informed Meta-Learning Framework for the Continuous Solution of Parametric PDEs on Arbitrary Geometries","created":"2025-04-03","cited_by_count":5,"area":null},{"id":"2406.02298","title":"Solving Partial Differential Equations in Different Domains by Operator Learning method Based on Boundary Integral Equations","created":"2024-06-04","cited_by_count":4,"area":null},{"id":"2501.01453","title":"Geometry Matters: Benchmarking Scientific ML Approaches for Flow Prediction around Complex Geometries","created":"2024-12-31","cited_by_count":3,"area":null},{"id":"2503.17289","title":"3D Neural Operator-Based Flow Surrogates around 3D geometries: Signed Distance Functions and Derivative Constraints","created":"2025-03-21","cited_by_count":3,"area":null},{"id":"2602.11626","title":"ArGEnT: Arbitrary Geometry-encoded Transformer for Operator Learning","created":"2026-02-12","cited_by_count":3,"area":null},{"id":"2407.03925","title":"Learning Lagrangian Interaction Dynamics with Sampling-Based Model Order Reduction","created":"2024-07-04","cited_by_count":2,"area":null},{"id":"2412.01379","title":"A deformation-based framework for learning solution mappings of PDEs defined on varying domains","created":"2024-12-02","cited_by_count":2,"area":null},{"id":"2506.17230","title":"MMET: A Multi-Input and Multi-Scale Transformer for Efficient PDEs Solving","created":"2025-05-24","cited_by_count":2,"area":null},{"id":"2507.03660","title":"Single vs. Multiple Branches in DeepONet and S-DeepONet: Network Architecture Follows Coupling in Multiphysics Systems","created":"2025-07-04","cited_by_count":2,"area":null},{"id":"2507.05291","title":"Physics-Informed Graph Neural Networks to Reconstruct Local Fields Considering Finite Strain Hyperelasticity","created":"2025-07-05","cited_by_count":2,"area":null},{"id":"2510.09685","title":"Deep Neural Networks Inspired by Differential Equations","created":"2025-10-09","cited_by_count":2,"area":null},{"id":"2505.01281","title":"A Physics-preserved Transfer Learning Method for Differential Equations","created":"2025-05-02","cited_by_count":1,"area":null},{"id":"2509.08515","title":"Variational Rank Reduction Autoencoders for Generative Thermal Design","created":"2025-09-10","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":"2405.03427","title":"Geometry-aware framework for deep energy method: an application to structural mechanics with hyperelastic materials","created":"2024-05-06","cited_by_count":0,"area":null},{"id":"2506.11761","title":"Using Deep Operators to Create Spatio-temporal Surrogates for Dynamical Systems under Uncertainty","created":"2025-06-13","cited_by_count":0,"area":null},{"id":"2506.12652","title":"Learning Mappings in Mesh-based Simulations","created":"2025-06-14","cited_by_count":0,"area":null},{"id":"2507.13902","title":"Deep Micro Solvers for Rough-Wall Stokes Flow in a Heterogeneous Multiscale Method","created":"2025-07-18","cited_by_count":0,"area":null},{"id":"2509.13520","title":"Learning Nonlinear Responses in PET Bottle Buckling with a Hybrid DeepONet-Transolver Framework","created":"2025-09-16","cited_by_count":0,"area":null},{"id":"2512.04434","title":"Predicting Time-Dependent Flow Over Complex Geometries Using Operator Networks","created":"2025-12-04","cited_by_count":0,"area":null},{"id":"2602.23703","title":"A Boundary Integral-based Neural Operator for Mesh Deformation","created":"2026-02-27","cited_by_count":0,"area":null},{"id":"2605.00760","title":"Learning the Helmholtz equation operator with DeepONet for non-parametric 2D geometries","created":"2026-05-01","cited_by_count":0,"area":null},{"id":"2606.10909","title":"Non-linear mechanical field reconstruction coupling recurrent neural networks with physics-informed graph neural networks","created":"2026-06-09","cited_by_count":0,"area":null},{"id":"2606.16624","title":"GA-VINO: A Geometry-Aware Variational Physics-informed Neural Operator for Mindlin-Reissner Plates","created":"2026-06-15","cited_by_count":0,"area":null},{"id":"2606.16900","title":"Factorized Neural Operators Decompose Dynamic and Persistent Responses","created":"2026-06-15","cited_by_count":0,"area":null},{"id":"2606.18305","title":"Starter-Iterator Neural Operator: A Unified Architecture for High-Fidelity Forward and Inverse PDE Problems","created":"2026-06-16","cited_by_count":0,"area":null},{"id":"2607.06976","title":"Hybrid Least Squares/Gradient Descent Methods for MIONets","created":"2026-07-08","cited_by_count":0,"area":null},{"id":"2607.25790","title":"SpectONet: A Physics-Guided Spectral Deep Operator Network for Euler-Bernoulli Beam Dynamics","created":"2026-07-28","cited_by_count":0,"area":null},{"id":"2607.28762","title":"Feature Interaction Modeling for Neural Operators","created":"2026-07-30","cited_by_count":0,"area":null},{"id":"2608.13827","title":"HI-MeshGraphNets: Efficient and Accurate Mesh-based Physics Learning with Hierarchical Multi-scale Graph Neural Networks","created":"2026-08-13","cited_by_count":0,"area":null},{"id":"2609.33715","title":"Theory Guided and Interpretable Neural Operator Design for Partial Differential Equation Learning","created":"2026-09-27","cited_by_count":0,"area":null}],"next_cursor":null}