{"total":814,"rows":[{"id":"2104.13478","title":"Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges","created":"2021-04-27","cited_by_count":1167,"area":null},{"id":"2207.05209","title":"Fourier Neural Operator with Learned Deformations for PDEs on General Geometries","created":"2022-07-11","cited_by_count":427,"area":null},{"id":"2108.08481","title":"Neural Operator: Learning Maps Between Function Spaces","created":"2021-08-19","cited_by_count":414,"area":null},{"id":"2402.02366","title":"Transolver: A Fast Transformer Solver for PDEs on General Geometries","created":"2024-02-04","cited_by_count":326,"area":null},{"id":"2309.00583","title":"Geometry-Informed Neural Operator for Large-Scale 3D PDEs","created":"2023-09-01","cited_by_count":303,"area":null},{"id":"2205.13671","title":"Transformer for Partial Differential Equations' Operator Learning","created":"2022-05-26","cited_by_count":283,"area":null},{"id":"2102.09544","title":"Combinatorial optimization and reasoning with graph neural networks","created":"2021-02-18","cited_by_count":268,"area":null},{"id":"2212.12794","title":"GraphCast: Learning skillful medium-range global weather forecasting","created":"2022-12-24","cited_by_count":267,"area":null},{"id":"2308.15560","title":"WeatherBench 2: A benchmark for the next generation of data-driven global weather models","created":"2023-08-29","cited_by_count":236,"area":null},{"id":"2110.02905","title":"Geometric and Physical Quantities Improve E(3) Equivariant Message Passing","created":"2021-10-06","cited_by_count":228,"area":null},{"id":"2405.19101","title":"Poseidon: Efficient Foundation Models for PDEs","created":"2024-05-29","cited_by_count":200,"area":null},{"id":"2303.08133","title":"MeshDiffusion: Score-based Generative 3D Mesh Modeling","created":"2023-03-14","cited_by_count":199,"area":null},{"id":"2209.15616","title":"Towards Multi-spatiotemporal-scale Generalized PDE Modeling","created":"2022-09-30","cited_by_count":197,"area":null},{"id":"2202.07575","title":"Forecasting Global Weather with Graph Neural Networks","created":"2022-02-15","cited_by_count":168,"area":null},{"id":"2303.13047","title":"Towards Better Dynamic Graph Learning: New Architecture and Unified Library","created":"2023-03-23","cited_by_count":157,"area":null},{"id":"2305.17560","title":"Scalable Transformer for PDE Surrogate Modeling","created":"2023-05-27","cited_by_count":128,"area":null},{"id":"2206.00133","title":"Pre-training via Denoising for Molecular Property Prediction","created":"2022-05-31","cited_by_count":124,"area":null},{"id":"2107.12375","title":"Geometric Deep Learning on Molecular Representations","created":"2021-07-26","cited_by_count":113,"area":null},{"id":"2110.10863","title":"Deep Generative Models in Engineering Design: A Review","created":"2021-10-21","cited_by_count":105,"area":null},{"id":"2106.14103","title":"PhyCRNet: Physics-informed Convolutional-Recurrent Network for Solving Spatiotemporal PDEs","created":"2021-06-26","cited_by_count":98,"area":null},{"id":"2210.00612","title":"MultiScale MeshGraphNets","created":"2022-10-02","cited_by_count":98,"area":null},{"id":"2310.02994","title":"Multiple Physics Pretraining for Physical Surrogate Models","created":"2023-10-04","cited_by_count":98,"area":null},{"id":"2201.11969","title":"Approximately Equivariant Networks for Imperfectly Symmetric Dynamics","created":"2022-01-28","cited_by_count":97,"area":null},{"id":"2209.04934","title":"Clifford Neural Layers for PDE Modeling","created":"2022-09-08","cited_by_count":92,"area":null},{"id":"2211.08064","title":"Physics-Informed Machine Learning: A Survey on Problems, Methods and Applications","created":"2022-11-15","cited_by_count":92,"area":null},{"id":"2112.15275","title":"Learned Coarse Models for Efficient Turbulence Simulation","created":"2021-12-31","cited_by_count":91,"area":null},{"id":"2407.07218","title":"Weak baselines and reporting biases lead to overoptimism in machine learning for fluid-related partial differential equations","created":"2024-07-09","cited_by_count":91,"area":null},{"id":"2503.17973","title":"PhysTwin: Physics-Informed Reconstruction and Simulation of Deformable Objects from Videos","created":"2025-03-23","cited_by_count":87,"area":null},{"id":"2201.09113","title":"Predicting Physics in Mesh-reduced Space with Temporal Attention","created":"2022-01-22","cited_by_count":84,"area":null},{"id":"2306.07266","title":"Operator Learning with Neural Fields: Tackling PDEs on General Geometries","created":"2023-06-12","cited_by_count":84,"area":null},{"id":"2212.07564","title":"AirfRANS: High Fidelity Computational Fluid Dynamics Dataset for Approximating Reynolds-Averaged Navier-Stokes Solutions","created":"2022-12-15","cited_by_count":82,"area":null},{"id":"2106.05150","title":"Scaling Up Graph Neural Networks Via Graph Coarsening","created":"2021-06-09","cited_by_count":80,"area":null},{"id":"2212.07242","title":"HOOD: Hierarchical Graphs for Generalized Modelling of Clothing Dynamics","created":"2022-12-14","cited_by_count":78,"area":null},{"id":"2303.10462","title":"Machine learning with data assimilation and uncertainty quantification for dynamical systems: a review","created":"2023-03-18","cited_by_count":75,"area":null},{"id":"2209.14855","title":"Continuous PDE Dynamics Forecasting with Implicit Neural Representations","created":"2022-09-29","cited_by_count":73,"area":null},{"id":"2202.02306","title":"Rediscovering orbital mechanics with machine learning","created":"2022-02-04","cited_by_count":71,"area":null},{"id":"2203.06442","title":"Equivariant Graph Mechanics Networks with Constraints","created":"2022-03-12","cited_by_count":68,"area":null},{"id":"2203.16797","title":"When Physics Meets Machine Learning: A Survey of Physics-Informed Machine Learning","created":"2022-03-31","cited_by_count":65,"area":null},{"id":"2202.11855","title":"Learning Multi-Object Dynamics with Compositional Neural Radiance Fields","created":"2022-02-24","cited_by_count":63,"area":null},{"id":"2105.10389","title":"Learning Visible Connectivity Dynamics for Cloth Smoothing","created":"2021-05-21","cited_by_count":62,"area":null},{"id":"2507.12144","title":"FourCastNet 3: A geometric approach to probabilistic machine-learning weather forecasting at scale","created":"2025-07-16","cited_by_count":62,"area":null},{"id":"2106.04781","title":"Encoding physics to learn reaction-diffusion processes","created":"2021-06-09","cited_by_count":61,"area":null},{"id":"2302.01018","title":"Graph Neural Networks for temporal graphs: State of the art, open challenges, and opportunities","created":"2023-02-02","cited_by_count":61,"area":null},{"id":"2302.10803","title":"Eagle: Large-Scale Learning of Turbulent Fluid Dynamics with Mesh Transformers","created":"2023-02-16","cited_by_count":57,"area":null},{"id":"2304.14369","title":"Learning Neural Constitutive Laws From Motion Observations for Generalizable PDE Dynamics","created":"2023-04-27","cited_by_count":57,"area":null},{"id":"2208.13273","title":"Blending Neural Operators and Relaxation Methods in PDE Numerical Solvers","created":"2022-08-28","cited_by_count":54,"area":null},{"id":"2407.07889","title":"AdaptiGraph: Material-Adaptive Graph-Based Neural Dynamics for Robotic Manipulation","created":"2024-07-10","cited_by_count":54,"area":null},{"id":"2306.01776","title":"From Zero to Turbulence: Generative Modeling for 3D Flow Simulation","created":"2023-05-29","cited_by_count":49,"area":null},{"id":"2210.02573","title":"Efficient Learning of Mesh-Based Physical Simulation with BSMS-GNN","created":"2022-10-05","cited_by_count":45,"area":null},{"id":"2106.07971","title":"Simple GNN Regularisation for 3D Molecular Property Prediction & Beyond","created":"2021-06-15","cited_by_count":44,"area":null},{"id":"2107.01272","title":"Physics-Guided Deep Learning for Dynamical Systems: A Survey","created":"2021-07-02","cited_by_count":44,"area":null},{"id":"2203.17275","title":"DiffSkill: Skill Abstraction from Differentiable Physics for Deformable Object Manipulations with Tools","created":"2022-03-31","cited_by_count":44,"area":null},{"id":"2212.03574","title":"Learning rigid dynamics with face interaction graph networks","created":"2022-12-07","cited_by_count":43,"area":null},{"id":"2410.18912","title":"Dynamic 3D Gaussian Tracking for Graph-Based Neural Dynamics Modeling","created":"2024-10-24","cited_by_count":43,"area":null},{"id":"2102.10782","title":"NTopo: Mesh-free Topology Optimization using Implicit Neural Representations","created":"2021-02-22","cited_by_count":41,"area":null},{"id":"2305.08048","title":"Towards Understanding the Generalization of Graph Neural Networks","created":"2023-05-14","cited_by_count":41,"area":null},{"id":"2106.04900","title":"Simulating Continuum Mechanics with Multi-Scale Graph Neural Networks","created":"2021-06-09","cited_by_count":40,"area":null},{"id":"2303.02346","title":"FluidLab: A Differentiable Environment for Benchmarking Complex Fluid Manipulation","created":"2023-03-04","cited_by_count":40,"area":null},{"id":"2103.01342","title":"Reinforcement Learning for Adaptive Mesh Refinement","created":"2021-03-01","cited_by_count":39,"area":null},{"id":"2109.07719","title":"Efficient Differentiable Simulation of Articulated Bodies","created":"2021-09-16","cited_by_count":39,"area":null},{"id":"2206.07681","title":"Learning to Accelerate Partial Differential Equations via Latent Global Evolution","created":"2022-06-15","cited_by_count":39,"area":null},{"id":"2210.06876","title":"Learning Physical Dynamics with Subequivariant Graph Neural Networks","created":"2022-10-13","cited_by_count":39,"area":null},{"id":"2406.02176","title":"AROMA: Preserving Spatial Structure for Latent PDE Modeling with Local Neural Fields","created":"2024-06-04","cited_by_count":39,"area":null},{"id":"2505.08783","title":"CodePDE: An Inference Framework for LLM-driven PDE Solver Generation","created":"2025-05-13","cited_by_count":39,"area":null},{"id":"2102.10271","title":"Meta-Learning Dynamics Forecasting Using Task Inference","created":"2021-02-20","cited_by_count":38,"area":null},{"id":"2109.07143","title":"Spline-PINN: Approaching PDEs without Data using Fast, Physics-Informed Hermite-Spline CNNs","created":"2021-09-15","cited_by_count":38,"area":null},{"id":"2304.14118","title":"Learning Neural PDE Solvers with Parameter-Guided Channel Attention","created":"2023-04-27","cited_by_count":38,"area":null},{"id":"2401.11037","title":"Equivariant Graph Neural Operator for Modeling 3D Dynamics","created":"2024-01-19","cited_by_count":38,"area":null},{"id":"2202.00728","title":"Physical Design using Differentiable Learned Simulators","created":"2022-02-01","cited_by_count":37,"area":null},{"id":"2204.03216","title":"Neural Implicit Flow: a mesh-agnostic dimensionality reduction paradigm of spatio-temporal data","created":"2022-04-07","cited_by_count":37,"area":null},{"id":"2206.00711","title":"Learning to Solve PDE-constrained Inverse Problems with Graph Networks","created":"2022-06-01","cited_by_count":37,"area":null},{"id":"2311.00457","title":"Single-view 3D Scene Reconstruction with High-fidelity Shape and Texture","created":"2023-11-01","cited_by_count":37,"area":null},{"id":"2212.11220","title":"Neural Cloth Simulation","created":"2022-12-13","cited_by_count":36,"area":null},{"id":"2210.00124","title":"Implicit Neural Spatial Representations for Time-dependent PDEs","created":"2022-09-30","cited_by_count":35,"area":null},{"id":"2203.08852","title":"Learning the Dynamics of Physical Systems from Sparse Observations with Finite Element Networks","created":"2022-03-16","cited_by_count":34,"area":null},{"id":"2206.02881","title":"Mesh-based Dynamics with Occlusion Reasoning for Cloth Manipulation","created":"2022-06-06","cited_by_count":34,"area":null},{"id":"2502.00338","title":"OneForecast: A Universal Framework for Global and Regional Weather Forecasting","created":"2025-02-01","cited_by_count":34,"area":null},{"id":"2112.03383","title":"Graph Neural Networks Accelerated Molecular Dynamics","created":"2021-12-06","cited_by_count":33,"area":null},{"id":"2203.01874","title":"Thermodynamics-informed graph neural networks","created":"2022-03-03","cited_by_count":33,"area":null},{"id":"2205.11912","title":"Physics-Embedded Neural Networks: Graph Neural PDE Solvers with Mixed Boundary Conditions","created":"2022-05-24","cited_by_count":33,"area":null},{"id":"2403.08055","title":"DrivAerNet: A Parametric Car Dataset for Data-Driven Aerodynamic Design and Prediction","created":"2024-03-12","cited_by_count":33,"area":null},{"id":"2202.08956","title":"GNN-Surrogate: A Hierarchical and Adaptive Graph Neural Network for Parameter Space Exploration of Unstructured-Mesh Ocean Simulations","created":"2022-02-18","cited_by_count":32,"area":null},{"id":"2410.08257","title":"Neural Material Adaptor for Visual Grounding of Intrinsic Dynamics","created":"2024-10-10","cited_by_count":32,"area":null},{"id":"2401.07364","title":"PDE Generalization of In-Context Operator Networks: A Study on 1D Scalar Nonlinear Conservation Laws","created":"2024-01-14","cited_by_count":31,"area":null},{"id":"2104.05512","title":"One-shot learning for solution operators of partial differential equations","created":"2021-04-06","cited_by_count":30,"area":null},{"id":"2210.06036","title":"Guaranteed Conservation of Momentum for Learning Particle-based Fluid Dynamics","created":"2022-10-12","cited_by_count":30,"area":null},{"id":"2304.14391","title":"Energy-based Models are Zero-Shot Planners for Compositional Scene Rearrangement","created":"2023-04-27","cited_by_count":30,"area":null},{"id":"2402.00059","title":"FengWu-GHR: Learning the Kilometer-scale Medium-range Global Weather Forecasting","created":"2024-01-28","cited_by_count":30,"area":null},{"id":"2511.04665","title":"Real-to-Sim Robot Policy Evaluation with Gaussian Splatting Simulation of Soft-Body Interactions","created":"2025-11-06","cited_by_count":30,"area":null},{"id":"2110.00792","title":"Learning Models as Functionals of Signed-Distance Fields for Manipulation Planning","created":"2021-10-02","cited_by_count":29,"area":null},{"id":"2205.09702","title":"Parallel and Distributed Graph Neural Networks: An In-Depth Concurrency Analysis","created":"2022-05-19","cited_by_count":29,"area":null},{"id":"2208.11905","title":"Neural Novel Actor: Learning a Generalized Animatable Neural Representation for Human Actors","created":"2022-08-25","cited_by_count":28,"area":null},{"id":"2311.06253","title":"Advancing Parsimonious Deep Learning Weather Prediction using the HEALPix Mesh","created":"2023-09-11","cited_by_count":28,"area":null},{"id":"2408.12171","title":"Recent Advances on Machine Learning for Computational Fluid Dynamics: A Survey","created":"2024-08-22","cited_by_count":28,"area":null},{"id":"2112.09161","title":"Constraint-based graph network simulator","created":"2021-12-16","cited_by_count":27,"area":null},{"id":"2308.13212","title":"SEGNO: Generalizing Equivariant Graph Neural Networks with Physical Inductive Biases","created":"2023-08-25","cited_by_count":27,"area":null},{"id":"2309.16342","title":"LagrangeBench: A Lagrangian Fluid Mechanics Benchmarking Suite","created":"2023-09-28","cited_by_count":27,"area":null},{"id":"2402.12365","title":"Universal Physics Transformers: A Framework For Efficiently Scaling Neural Operators","created":"2024-02-19","cited_by_count":27,"area":null},{"id":"2402.12971","title":"Differentiability in Unrolled Training of Neural Physics Simulators on Transient Dynamics","created":"2024-02-20","cited_by_count":27,"area":null},{"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}],"next_cursor":"100"}