{"total":80,"rows":[{"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":"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":"2409.00230","title":"Spatially-Aware Diffusion Models with Cross-Attention for Global Field Reconstruction with Sparse Observations","created":"2024-08-30","cited_by_count":13,"area":"diffusion"},{"id":"2511.06294","title":"Transolver is a Linear Transformer: Revisiting Physics-Attention through the Lens of Linear Attention","created":"2025-11-09","cited_by_count":13,"area":null},{"id":"2506.16656","title":"Mesh-Informed Neural Operator : A Transformer Generative Approach","created":"2025-06-20","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":"2411.09678","title":"NeuralDEM -- Real-time Simulation of Industrial Particulate Flows","created":"2024-11-14","cited_by_count":10,"area":null},{"id":"2501.08428","title":"Physics-Informed Latent Neural Operator for Real-time Predictions of time-dependent parametric PDEs","created":"2025-01-14","cited_by_count":9,"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":"2508.12594","title":"FLARE: Fast Low-rank Attention Routing Engine","created":"2025-08-18","cited_by_count":6,"area":null},{"id":"2510.15280","title":"Foundation Models for Scientific Discovery: From Paradigm Enhancement to Paradigm Transition","created":"2025-10-17","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":"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":"2410.20100","title":"Latent Neural Operator Pretraining for Solving Time-Dependent PDEs","created":"2024-10-26","cited_by_count":5,"area":null},{"id":"2602.13616","title":"DiffusionRollout: Uncertainty-Aware Rollout Planning in Long-Horizon PDE Solving","created":"2026-02-14","cited_by_count":5,"area":"diffusion"},{"id":"2505.22573","title":"FNOPE: Simulation-based inference on function spaces with Fourier Neural Operators","created":"2025-05-28","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":"2502.08683","title":"A Deep Learning approach for parametrized and time dependent Partial Differential Equations using Dimensionality Reduction and Neural ODEs","created":"2025-02-12","cited_by_count":3,"area":null},{"id":"2510.05874","title":"MaNGO - Adaptable Graph Network Simulators via Meta-Learning","created":"2025-10-07","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":"2605.14546","title":"Discovering Physical Directions in Weight Space: Composing Neural PDE Experts","created":"2026-05-14","cited_by_count":3,"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":"2511.03241","title":"A unified physics-informed generative operator framework for general inverse problems","created":"2025-11-05","cited_by_count":2,"area":null},{"id":"2511.09729","title":"Generalizing PDE Emulation with Equation-Aware Neural Operators","created":"2025-11-12","cited_by_count":2,"area":null},{"id":"2512.04974","title":"Efficient Generative Transformer Operators For Million-Point PDEs","created":"2025-12-04","cited_by_count":2,"area":null},{"id":"2602.21551","title":"From Basis to Basis: Gaussian Particle Representation for Interpretable PDE Operators","created":"2026-02-25","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":"2605.25353","title":"PDEInvBench: A Comprehensive Dataset and Design Space Exploration of Neural Networks for PDE Inverse Problems","created":"2026-05-25","cited_by_count":2,"area":null},{"id":"2605.25413","title":"Autoregression-Free Neural Operators for Time-Dependent PDEs","created":"2026-05-25","cited_by_count":2,"area":null},{"id":"2504.08766","title":"Towards scientific machine learning for granular material simulations -- challenges and opportunities","created":"2025-04-01","cited_by_count":1,"area":null},{"id":"2505.04738","title":"SetONet: A Set-Based Operator Network for Solving PDEs with Variable-Input Sampling","created":"2025-05-07","cited_by_count":1,"area":null},{"id":"2506.12541","title":"BSA: Ball Sparse Attention for Large-scale Geometries","created":"2025-06-14","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":"2509.12401","title":"Reduced Order Modeling of Energetic Materials Using Physics-Aware Recurrent Convolutional Neural Networks in a Latent Space (LatentPARC)","created":"2025-09-15","cited_by_count":1,"area":null},{"id":"2509.25788","title":"From Cheap Geometry to Expensive Physics: Elevating Neural Operators via Latent Shape Pretraining","created":"2025-09-30","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":"2601.15547","title":"Learning Neural Operators from Partial Observations via Latent Autoregressive Modeling","created":"2026-01-22","cited_by_count":1,"area":null},{"id":"2605.08436","title":"A meshfree exterior calculus for generalizable and data-efficient learning of physics from point clouds","created":"2026-05-08","cited_by_count":1,"area":null},{"id":"2605.10154","title":"Stable Long-Horizon PDE Forecasting via Latent Structured Spectral Propagators","created":"2026-05-11","cited_by_count":1,"area":null},{"id":"2605.16966","title":"Harnessing AI for Inverse Partial Differential Equation Problems: Past, Present, and Prospects","created":"2026-05-16","cited_by_count":1,"area":null},{"id":"2607.01128","title":"GAIA: Geometry-Adaptive Operator Learning for Forward and Inverse Problems","created":"2026-07-01","cited_by_count":1,"area":null},{"id":"2607.18043","title":"Adaptive Mamba Neural Operators","created":"2026-07-20","cited_by_count":1,"area":"ssm"},{"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":"2505.11682","title":"Mollifier Layers: Enabling Efficient High-Order Derivatives in Inverse PDE Learning","created":"2025-05-16","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":"2510.16071","title":"MNO: Multiscale Neural Operator for 3D Computational Fluid Dynamics","created":"2025-10-17","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":"2605.07375","title":"QuadNorm: Resolution-Robust Normalization for Neural Operators","created":"2026-05-08","cited_by_count":0,"area":null},{"id":"2605.09523","title":"HS-FNO: History-Space Fourier Neural Operator for Non-Markovian Partial Differential Equations","created":"2026-05-10","cited_by_count":0,"area":null},{"id":"2605.11970","title":"NOFE - Neural Operator Function Embedding","created":"2026-05-12","cited_by_count":0,"area":null},{"id":"2605.12368","title":"MetaColloc: Optimization-Free PDE Solving via Meta-Learned Basis Functions","created":"2026-05-12","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":"2605.20978","title":"Point Cloud Sequence Encoding for Material-conditioned Graph Network Simulators","created":"2026-05-20","cited_by_count":0,"area":null},{"id":"2605.22182","title":"IKNO: Infinite-order Kernel Neural Operators","created":"2026-05-21","cited_by_count":0,"area":null},{"id":"2605.25115","title":"Courant: a State-Adaptive Perceiver-Based Neural Surrogate with Local Support and Interpretable Field Decomposition","created":"2026-05-24","cited_by_count":0,"area":null},{"id":"2605.28368","title":"LEIA: Learned Environment for Interactive Architected Materials","created":"2026-05-27","cited_by_count":0,"area":"wm"},{"id":"2605.31013","title":"Physics-Informed Coarsening for Multigrid Graph Neural Surrogates","created":"2026-05-29","cited_by_count":0,"area":null},{"id":"2605.31559","title":"Functional Attention: From Pairwise Affinities to Functional Correspondences","created":"2026-05-29","cited_by_count":0,"area":null},{"id":"2606.19984","title":"Kolmogorov-Arnold Reservoir Computing","created":"2026-06-18","cited_by_count":0,"area":null},{"id":"2607.07718","title":"LLT: Local Linear Transformer for PDE Operator Learning","created":"2026-07-04","cited_by_count":0,"area":null},{"id":"2607.08025","title":"PGD-NO: A Neural Operator with Precomputed Geometry Decomposition for 3D Million-scale Physics Simulations","created":"2026-07-09","cited_by_count":0,"area":null},{"id":"2607.27188","title":"Inverse Learning of Latent Risk-Neutral Densities from Irregular Option Quotes","created":"2026-07-29","cited_by_count":0,"area":null},{"id":"2608.01164","title":"Hybrid Lagrangian-Eulerian Model for Lagrangian Fluid Simulation","created":"2026-08-02","cited_by_count":0,"area":null},{"id":"2608.01839","title":"tFUSOperator: Operator Learning for Transcranial Focused Ultrasound Digital Twins","created":"2026-08-03","cited_by_count":0,"area":null},{"id":"2608.09764","title":"MoNo: Multiscale Optimal Transport Neural Operator for Solving PDEs on General Geometries","created":"2026-08-10","cited_by_count":0,"area":null},{"id":"2608.11237","title":"Geometry-aware Incremental Neural Operator for Long-Horizon PDE prediction","created":"2026-07-31","cited_by_count":0,"area":null},{"id":"2608.11519","title":"FLARE++: Low-rank attention with dynamic attention routing","created":"2026-08-12","cited_by_count":0,"area":null},{"id":"2608.14120","title":"From Fixed Grids to Moving Particles:A Transferable Latent Operator for Fluid Dynamics","created":"2026-08-14","cited_by_count":0,"area":null},{"id":"2608.21677","title":"Read, Write, Relax: Why Neural PDE Surrogates Need Both Global and Local Processing","created":"2026-08-21","cited_by_count":0,"area":null},{"id":"2609.16853","title":"Can Deep Learning Achieve Cross-Physics Mapping?","created":"2026-09-15","cited_by_count":0,"area":null},{"id":"2609.18419","title":"HiLNO: A Hierarchical Latent Neural Operator with Multi-Scale Supervision for PDEs on General Geometries","created":"2026-09-16","cited_by_count":0,"area":null},{"id":"2609.35752","title":"Neural Harmonic Measure Operator","created":"2026-09-28","cited_by_count":0,"area":null},{"id":"2609.39914","title":"Cluster Attention Neural Operators for Solving Parametric Partial Differential Equations","created":"2026-09-30","cited_by_count":0,"area":null},{"id":"2610.01697","title":"Learning PDE Dynamics between Submanifolds Using Green's Observation Operators","created":"2026-10-01","cited_by_count":0,"area":null},{"id":"2610.10398","title":"Cross-Domain Pretraining for Steady-State Neural CFD Surrogates","created":"2026-10-07","cited_by_count":0,"area":null},{"id":"2610.12618","title":"Mesh-Native Tokenisation: Retrofitting Foundation Models to Unstructured Data","created":"2026-10-08","cited_by_count":0,"area":null},{"id":"2610.13334","title":"PDE-PFN: Prior-Data Fitted Neural PDE Solver","created":"2026-10-09","cited_by_count":0,"area":null},{"id":"2610.14537","title":"Learn the Dynamics, Enforce the Physics: Generative Latent Operators for High-Dimensional Beam Evolution","created":"2026-10-10","cited_by_count":0,"area":null},{"id":"2610.20400","title":"HRTF Upsampling and Personalization via Neural Operators","created":"2026-10-14","cited_by_count":0,"area":null}],"next_cursor":null}