{"total":117,"rows":[{"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":"2310.12487","title":"Improved Operator Learning by Orthogonal Attention","created":"2023-10-19","cited_by_count":61,"area":null},{"id":"2403.12553","title":"Pretraining Codomain Attention Neural Operators for Solving Multiphysics PDEs","created":"2024-03-19","cited_by_count":59,"area":null},{"id":"2502.09692","title":"AB-UPT: Scaling Neural CFD Surrogates for High-Fidelity Automotive Aerodynamics Simulations via Anchored-Branched Universal Physics Transformers","created":"2025-02-13","cited_by_count":53,"area":null},{"id":"2405.17527","title":"Unisolver: PDE-Conditional Transformers Towards Universal Neural PDE Solvers","created":"2024-05-27","cited_by_count":35,"area":null},{"id":"2505.24717","title":"PDE-Transformer: Efficient and Versatile Transformers for Physics Simulations","created":"2025-05-30","cited_by_count":32,"area":null},{"id":"2409.02313","title":"On the Benefits of Memory for Modeling Time-Dependent PDEs","created":"2024-09-03","cited_by_count":31,"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":"2502.17019","title":"Erwin: A Tree-based Hierarchical Transformer for Large-scale Physical Systems","created":"2025-02-24","cited_by_count":22,"area":null},{"id":"2502.11835","title":"Neural Chaos: A Spectral Stochastic Neural Operator","created":"2025-02-17","cited_by_count":21,"area":null},{"id":"2602.04940","title":"Transolver-3: Scaling Up Transformer Solvers to Industrial-Scale Geometries","created":"2026-02-04","cited_by_count":20,"area":null},{"id":"2311.02225","title":"Multi-scale Time-stepping of Partial Differential Equations with Transformers","created":"2023-11-03","cited_by_count":14,"area":null},{"id":"2406.08473","title":"Strategies for Pretraining Neural Operators","created":"2024-06-12","cited_by_count":14,"area":null},{"id":"2411.00040","title":"P$^2$C$^2$Net: PDE-Preserved Coarse Correction Network for efficient prediction of spatiotemporal dynamics","created":"2024-10-29","cited_by_count":13,"area":null},{"id":"2406.03919","title":"Vectorized Conditional Neural Fields: A Framework for Solving Time-dependent Parametric Partial Differential Equations","created":"2024-06-06","cited_by_count":12,"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":"2506.10973","title":"Principled Approaches for Extending Neural Architectures to Function Spaces for Operator Learning","created":"2025-06-12","cited_by_count":12,"area":null},{"id":"2405.12202","title":"Hierarchical Neural Operator Transformer with Learnable Frequency-aware Loss Prior for Arbitrary-scale Super-resolution","created":"2024-05-20","cited_by_count":11,"area":null},{"id":"2405.17211","title":"Spectral-Refiner: Accurate Fine-Tuning of Spatiotemporal Fourier Neural Operator for Turbulent Flows","created":"2024-05-27","cited_by_count":11,"area":null},{"id":"2410.11382","title":"Holistic Physics Solver: Learning PDEs in a Unified Spectral-Physical Space","created":"2024-10-15","cited_by_count":10,"area":null},{"id":"2602.20399","title":"GeoPT: Scaling Physics Simulation via Lifted Geometric Pre-Training","created":"2026-02-23","cited_by_count":10,"area":null},{"id":"2402.16014","title":"OmniArch: Building Foundation Model For Scientific Computing","created":"2024-02-25","cited_by_count":9,"area":null},{"id":"2405.14558","title":"FUSE: Fast Unified Simulation and Estimation for PDEs","created":"2024-05-23","cited_by_count":8,"area":null},{"id":"2412.13074","title":"Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates","created":"2024-12-17","cited_by_count":8,"area":null},{"id":"2502.08574","title":"TANTE: Time-Adaptive Operator Learning via Neural Taylor Expansion","created":"2025-02-12","cited_by_count":8,"area":null},{"id":"2503.10257","title":"AMR-Transformer: Enabling Efficient Long-range Interaction for Complex Neural Fluid Simulation","created":"2025-03-13","cited_by_count":8,"area":null},{"id":"2509.12344","title":"FEDONet : Fourier-Embedded DeepONet for Spectrally Accurate Operator Learning","created":"2025-09-15","cited_by_count":8,"area":null},{"id":"2602.02788","title":"Structure-Preserving Learning Improves Geometry Generalization in Neural PDEs","created":"2026-02-02","cited_by_count":8,"area":null},{"id":"2310.10565","title":"HelmFluid: Learning Helmholtz Dynamics for Interpretable Fluid Prediction","created":"2023-10-16","cited_by_count":7,"area":null},{"id":"2410.01137","title":"Explain Like I'm Five: Using LLMs to Improve PDE Surrogate Models with Text","created":"2024-10-02","cited_by_count":7,"area":null},{"id":"2405.07395","title":"CaFA: Global Weather Forecasting with Factorized Attention on Sphere","created":"2024-05-12","cited_by_count":6,"area":null},{"id":"2508.12594","title":"FLARE: Fast Low-rank Attention Routing Engine","created":"2025-08-18","cited_by_count":6,"area":null},{"id":"2510.24173","title":"EddyFormer: Accelerated Neural Simulations of Three-Dimensional Turbulence at Scale","created":"2025-10-28","cited_by_count":6,"area":null},{"id":"2511.01924","title":"Neural Green's Functions","created":"2025-11-02","cited_by_count":6,"area":null},{"id":"2401.16327","title":"PICL: Physics Informed Contrastive Learning for Partial Differential Equations","created":"2024-01-29","cited_by_count":5,"area":null},{"id":"2402.17853","title":"Latent Neural PDE Solver: a reduced-order modelling framework for partial differential equations","created":"2024-02-27","cited_by_count":5,"area":null},{"id":"2403.13850","title":"Spatio-Temporal Fluid Dynamics Modeling via Physical-Awareness and Parameter Diffusion Guidance","created":"2024-03-18","cited_by_count":5,"area":null},{"id":"2405.17509","title":"Reference Neural Operators: Learning the Smooth Dependence of Solutions of PDEs on Geometric Deformations","created":"2024-05-27","cited_by_count":5,"area":null},{"id":"2407.11253","title":"Separable Operator Networks","created":"2024-07-15","cited_by_count":5,"area":null},{"id":"2407.17616","title":"Pretraining a Neural Operator in Lower Dimensions","created":"2024-07-24","cited_by_count":5,"area":null},{"id":"2410.20100","title":"Latent Neural Operator Pretraining for Solving Time-Dependent PDEs","created":"2024-10-26","cited_by_count":5,"area":null},{"id":"2411.03527","title":"PACE: Pacing Operator Learning to Accurate Optical Field Simulation for Complicated Photonic Devices","created":"2024-11-05","cited_by_count":5,"area":null},{"id":"2505.01736","title":"PeSANet: Physics-encoded Spectral Attention Network for Simulating PDE-Governed Complex Systems","created":"2025-05-03","cited_by_count":5,"area":null},{"id":"2505.10930","title":"Physics-informed Temporal Alignment for Auto-regressive PDE Foundation Models","created":"2025-05-16","cited_by_count":5,"area":null},{"id":"2505.20349","title":"FD-Bench: A Modular and Fair Benchmark for Data-driven Fluid Simulation","created":"2025-05-25","cited_by_count":5,"area":null},{"id":"2507.09264","title":"Overtone: Cyclic Patch Modulation for Clean, Efficient, and Flexible Physics Emulators","created":"2025-07-12","cited_by_count":5,"area":null},{"id":"2404.16882","title":"ThermoPore: Predicting Part Porosity Based on Thermal Images Using Deep Learning","created":"2024-04-23","cited_by_count":4,"area":null},{"id":"2407.00382","title":"Towards Universal Mesh Movement Networks","created":"2024-06-29","cited_by_count":4,"area":null},{"id":"2601.18707","title":"SMART: Scalable Mesh-free Aerodynamic Simulations from Raw Geometries using a Transformer-based Surrogate Model","created":"2026-01-26","cited_by_count":4,"area":null},{"id":"2604.03582","title":"Simple yet Effective: Low-Rank Spatial Attention for Neural Operators","created":"2026-04-04","cited_by_count":4,"area":null},{"id":"2402.02425","title":"DeepLag: Discovering Deep Lagrangian Dynamics for Intuitive Fluid Prediction","created":"2024-02-04","cited_by_count":3,"area":null},{"id":"2501.02791","title":"Orthogonal greedy algorithm for linear operator learning with shallow neural network","created":"2025-01-06","cited_by_count":3,"area":null},{"id":"2501.15987","title":"MultiPDENet: PDE-embedded Learning with Multi-time-stepping for Accelerated Flow Simulation","created":"2025-01-27","cited_by_count":3,"area":null},{"id":"2504.06070","title":"PINP: Physics-Informed Neural Predictor with latent estimation of fluid flows","created":"2025-04-08","cited_by_count":3,"area":null},{"id":"2506.06001","title":"LaDEEP: A Deep Learning-based Surrogate Model for Large Deformation of Elastic-Plastic Solids","created":"2025-06-06","cited_by_count":3,"area":null},{"id":"2509.23173","title":"F-Adapter: Frequency-Adaptive Parameter-Efficient Fine-Tuning in Scientific Machine Learning","created":"2025-09-27","cited_by_count":3,"area":null},{"id":"2512.01738","title":"MSPT: Efficient Large-Scale Physical Modeling via Parallelized Multi-Scale Attention","created":"2025-12-01","cited_by_count":3,"area":null},{"id":"2603.12635","title":"Adaptive Diffusion Posterior Sampling for Data and Model Fusion of Complex Nonlinear Dynamical Systems","created":"2026-03-13","cited_by_count":3,"area":null},{"id":"2412.18840","title":"Implicit factorized transformer approach to fast prediction of turbulent channel flows","created":"2024-12-25","cited_by_count":2,"area":null},{"id":"2502.06753","title":"SMRS: advocating a unified reporting standard for surrogate models in the artificial intelligence era","created":"2025-02-10","cited_by_count":2,"area":null},{"id":"2504.13480","title":"Integrating Locality-Aware Attention with Transformers for General Geometry PDEs","created":"2025-04-18","cited_by_count":2,"area":null},{"id":"2505.18857","title":"Hierarchical-embedding autoencoder with a predictor (HEAP) as efficient architecture for learning long-term evolution of complex multi-scale physical systems","created":"2025-05-24","cited_by_count":2,"area":null},{"id":"2506.13906","title":"GITO: Graph-Informed Transformer Operator for Learning Complex Partial Differential Equations","created":"2025-06-16","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":"2507.23428","title":"Merging Memory and Space: A State Space Neural Operator","created":"2025-07-31","cited_by_count":2,"area":null},{"id":"2508.04463","title":"GFocal: A Global-Focal Neural Operator for Solving PDEs on Arbitrary Geometries","created":"2025-08-06","cited_by_count":2,"area":null},{"id":"2605.13790","title":"Di-BiLPS: Denoising induced Bidirectional Latent-PDE-Solver under Sparse Observations","created":"2026-05-13","cited_by_count":2,"area":null},{"id":"2402.17185","title":"Inpainting Computational Fluid Dynamics with Deep Learning","created":"2024-02-27","cited_by_count":1,"area":null},{"id":"2405.20287","title":"Flexible SE(2) graph neural networks with applications to PDE surrogates","created":"2024-05-30","cited_by_count":1,"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":"2505.21573","title":"Spectral-inspired Operator Learning with Limited Data and Unknown Physics","created":"2025-05-27","cited_by_count":1,"area":null},{"id":"2507.01975","title":"Learnable-Differentiable Finite Volume Solver for Accelerated Simulation of Flows","created":"2025-06-23","cited_by_count":1,"area":null},{"id":"2510.02683","title":"Can Data-Driven Dynamics Reveal Hidden Physics? There Is A Need for Interpretable Neural Operators","created":"2025-10-03","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":"2601.17407","title":"Efficient Dilated Squeeze and Excitation Neural Operator for Differential Equations","created":"2026-01-24","cited_by_count":1,"area":null},{"id":"2601.21681","title":"LLM4Fluid: Large Language Models as Generalizable Neural Solvers for Fluid Dynamics","created":"2026-01-29","cited_by_count":1,"area":null},{"id":"2602.18248","title":"Neural-HSS: Hierarchical Semi-Separable Neural PDE Solver","created":"2026-02-20","cited_by_count":1,"area":null},{"id":"2605.09096","title":"Bridging Spectral Operator Learning and U-Net Hierarchies: SpectraNet for Stable Autoregressive PDE Surrogates","created":"2026-05-09","cited_by_count":1,"area":null},{"id":"2606.13443","title":"How Much Memory Do We Need? Adaptive Memory Gate for Neural Operators","created":"2026-06-11","cited_by_count":1,"area":null},{"id":"2608.04222","title":"TIDE: A Physically Diverse 3D Turbulence Benchmark Dataset for Advancing Scientific Machine Learning","created":"2026-08-04","cited_by_count":1,"area":null},{"id":"2608.06894","title":"From Points to Edges: Edge-Conditioned Spectral Operators for Physics-Sensitive PDE Learning","created":"2026-08-07","cited_by_count":1,"area":null},{"id":"2403.19570","title":"GrINd: Grid Interpolation Network for Scattered Observations","created":"2024-03-28","cited_by_count":0,"area":null},{"id":"2407.13010","title":"A Resolution Independent Neural Operator","created":"2024-07-17","cited_by_count":0,"area":null},{"id":"2409.19976","title":"Deep Parallel Spectral Neural Operators for Solving Partial Differential Equations with Enhanced Low-Frequency Learning Capability","created":"2024-09-30","cited_by_count":0,"area":null},{"id":"2504.15897","title":"SUPRA: Subspace Parameterized Attention for Neural Operator on General Domains","created":"2025-04-22","cited_by_count":0,"area":null},{"id":"2505.20454","title":"BlastOFormer: Attention and Neural Operator Deep Learning Methods for Explosive Blast Prediction","created":"2025-05-26","cited_by_count":0,"area":null},{"id":"2506.08226","title":"Mondrian: Transformer Operators via Domain Decomposition","created":"2025-06-09","cited_by_count":0,"area":null},{"id":"2506.10862","title":"OmniFluids: Physics Pre-trained Modeling of Fluid Dynamics","created":"2025-06-12","cited_by_count":0,"area":null},{"id":"2507.03863","title":"Enhanced accuracy through ensembling of randomly initialized auto-regressive models for time-dependent PDEs","created":"2025-07-05","cited_by_count":0,"area":null},{"id":"2507.07292","title":"Discretization-independent operator learning for partial differential equations","created":"2025-07-09","cited_by_count":0,"area":null},{"id":"2507.18067","title":"Multiscale Neural PDE Surrogates for Prediction and Downscaling: Application to Ocean Currents","created":"2025-07-24","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":"2512.11686","title":"Stable spectral neural operator for learning stiff PDE systems from limited data","created":"2025-12-12","cited_by_count":0,"area":null},{"id":"2512.23192","title":"PGOT: A Physics-Geometry Operator Transformer for Complex PDEs","created":"2025-12-29","cited_by_count":0,"area":null},{"id":"2603.03112","title":"From Complex Dynamics to DynFormer: Rethinking Transformers for PDEs","created":"2026-03-03","cited_by_count":0,"area":null},{"id":"2603.10466","title":"UniPINN: A Unified PINN Framework for Multi-task Learning of Diverse Navier-Stokes Equations","created":"2026-03-11","cited_by_count":0,"area":null},{"id":"2604.06475","title":"AE-ViT: Stable Long-Horizon Parametric Partial Differential Equations Modeling","created":"2026-04-07","cited_by_count":0,"area":null},{"id":"2604.15617","title":"A Structure-Preserving Graph Neural Solver for Parametric Hyperbolic Conservation Laws","created":"2026-04-17","cited_by_count":0,"area":null},{"id":"2605.04198","title":"Deep Wave Network for Modeling Multi-Scale Physical Dynamics","created":"2026-05-05","cited_by_count":0,"area":null},{"id":"2605.08843","title":"M$^3$: Reframing Training Measures for Discretized Physical Simulations","created":"2026-05-09","cited_by_count":0,"area":null}],"next_cursor":"100"}