{"total":84,"rows":[{"id":"2301.10343","title":"ClimaX: A foundation model for weather and climate","created":"2023-01-24","cited_by_count":337,"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":"2209.15616","title":"Towards Multi-spatiotemporal-scale Generalized PDE Modeling","created":"2022-09-30","cited_by_count":197,"area":null},{"id":"2305.17560","title":"Scalable Transformer for PDE Surrogate Modeling","created":"2023-05-27","cited_by_count":128,"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":"2404.10024","title":"ClimODE: Climate and Weather Forecasting with Physics-informed Neural ODEs","created":"2024-04-15","cited_by_count":82,"area":null},{"id":"2210.08566","title":"Theory for Equivariant Quantum Neural Networks","created":"2022-10-16","cited_by_count":62,"area":null},{"id":"2305.11141","title":"Clifford Group Equivariant Neural Networks","created":"2023-05-18","cited_by_count":55,"area":null},{"id":"2305.18415","title":"Geometric Algebra Transformer","created":"2023-05-28","cited_by_count":48,"area":null},{"id":"2210.10890","title":"Mitigating spectral bias for the multiscale operator learning","created":"2022-10-19","cited_by_count":44,"area":null},{"id":"2302.06594","title":"Geometric Clifford Algebra Networks","created":"2023-02-13","cited_by_count":38,"area":null},{"id":"2405.14806","title":"Lorentz-Equivariant Geometric Algebra Transformers for High-Energy Physics","created":"2024-05-23","cited_by_count":38,"area":null},{"id":"2301.11509","title":"Out-of-distributional risk bounds for neural operators with applications to the Helmholtz equation","created":"2023-01-27","cited_by_count":32,"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":"2402.12365","title":"Universal Physics Transformers: A Framework For Efficiently Scaling Neural Operators","created":"2024-02-19","cited_by_count":27,"area":null},{"id":"2402.14730","title":"Clifford-Steerable Convolutional Neural Networks","created":"2024-02-22","cited_by_count":25,"area":null},{"id":"2404.07200","title":"Toward a Better Understanding of Fourier Neural Operators from a Spectral Perspective","created":"2024-04-10","cited_by_count":25,"area":null},{"id":"2402.03541","title":"HAMLET: Graph Transformer Neural Operator for Partial Differential Equations","created":"2024-02-05","cited_by_count":24,"area":null},{"id":"2209.14977","title":"Transformer Meets Boundary Value Inverse Problems","created":"2022-09-29","cited_by_count":22,"area":null},{"id":"2402.10011","title":"Clifford Group Equivariant Simplicial Message Passing Networks","created":"2024-02-15","cited_by_count":22,"area":null},{"id":"2408.01536","title":"Active Learning for Neural PDE Solvers","created":"2024-08-02","cited_by_count":22,"area":null},{"id":"2303.17078","title":"Machine Learning for Partial Differential Equations","created":"2023-03-30","cited_by_count":21,"area":null},{"id":"2406.06660","title":"Space-Time Continuous PDE Forecasting using Equivariant Neural Fields","created":"2024-06-10","cited_by_count":18,"area":null},{"id":"2406.03164","title":"Topological Neural Networks go Persistent, Equivariant, and Continuous","created":"2024-06-05","cited_by_count":17,"area":null},{"id":"2402.05957","title":"Accelerating PDE Data Generation via Differential Operator Action in Solution Space","created":"2024-02-04","cited_by_count":15,"area":null},{"id":"2311.04744","title":"Euclidean, Projective, Conformal: Choosing a Geometric Algebra for Equivariant Transformers","created":"2023-11-08","cited_by_count":13,"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":"2305.15603","title":"Learning Lagrangian Fluid Mechanics with E($3$)-Equivariant Graph Neural Networks","created":"2023-05-24","cited_by_count":12,"area":null},{"id":"2401.09516","title":"Accelerating Data Generation for Neural Operators via Krylov Subspace Recycling","created":"2024-01-17","cited_by_count":12,"area":null},{"id":"2403.07536","title":"LaB-GATr: geometric algebra transformers for large biomedical surface and volume meshes","created":"2024-03-12","cited_by_count":12,"area":null},{"id":"2302.10255","title":"NeuralStagger: Accelerating Physics-constrained Neural PDE Solver with Spatial-temporal Decomposition","created":"2023-02-20","cited_by_count":11,"area":null},{"id":"2310.06379","title":"Understanding the Expressivity and Trainability of Fourier Neural Operator: A Mean-Field Perspective","created":"2023-10-10","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":"2507.02227","title":"PhysicsCorrect: A Training-Free Approach for Stable Neural PDE Simulations","created":"2025-07-03","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":"2303.16110","title":"Invariant preservation in machine learned PDE solvers via error correction","created":"2023-03-28","cited_by_count":7,"area":null},{"id":"2411.05238","title":"Generating Highly Designable Proteins with Geometric Algebra Flow Matching","created":"2024-11-07","cited_by_count":7,"area":null},{"id":"2504.14264","title":"Generative emulation of chaotic dynamics with coherent prior","created":"2025-04-19","cited_by_count":7,"area":null},{"id":"2601.06793","title":"CliffordNet: All You Need is Geometric Algebra","created":"2026-01-11","cited_by_count":7,"area":null},{"id":"2308.07350","title":"Efficient Neural PDE-Solvers using Quantization Aware Training","created":"2023-08-14","cited_by_count":6,"area":null},{"id":"2406.14995","title":"Differentiable and Learnable Wireless Simulation with Geometric Transformers","created":"2024-06-21","cited_by_count":6,"area":null},{"id":"2408.13222","title":"An Overview on Machine Learning Methods for Partial Differential Equations: from Physics Informed Neural Networks to Deep Operator Learning","created":"2024-08-23","cited_by_count":6,"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":"2406.04052","title":"Multivector Neurons: Better and Faster O(n)-Equivariant Clifford Graph Neural Networks","created":"2024-06-06","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":"2302.01191","title":"Noncommutative $C^*$-algebra Net: Learning Neural Networks with Powerful Product Structure in $C^*$-algebra","created":"2023-01-26","cited_by_count":4,"area":null},{"id":"2312.05153","title":"Uncertainty Quantification and Propagation in Surrogate-based Bayesian Inference","created":"2023-12-08","cited_by_count":4,"area":null},{"id":"2402.15656","title":"Learning Semilinear Neural Operators : A Unified Recursive Framework For Prediction And Data Assimilation","created":"2024-02-24","cited_by_count":4,"area":null},{"id":"2601.05525","title":"Explainable AI: Learning from the Learners","created":"2026-01-09","cited_by_count":4,"area":null},{"id":"2406.02597","title":"CoNO: Complex Neural Operator for Continous Dynamical Physical Systems","created":"2024-06-01","cited_by_count":3,"area":null},{"id":"2509.10363","title":"Physics-informed sensor coverage through structure preserving machine learning","created":"2025-09-12","cited_by_count":3,"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":"2506.09625","title":"GLGENN: A Novel Parameter-Light Equivariant Neural Networks Architecture Based on Clifford Geometric Algebras","created":"2025-06-11","cited_by_count":2,"area":null},{"id":"2507.11688","title":"Composing Linear Layers from Irreducibles","created":"2025-07-15","cited_by_count":2,"area":null},{"id":"2609.24737","title":"Ananke: Contractive Torus Attractor Networks","created":"2026-09-21","cited_by_count":2,"area":null},{"id":"2302.03286","title":"Algorithmically Designed Artificial Neural Networks (ADANNs): Higher order deep operator learning for parametric partial differential equations","created":"2023-02-07","cited_by_count":1,"area":null},{"id":"2311.00797","title":"Tipping Points of Evolving Epidemiological Networks: Machine Learning-Assisted, Data-Driven Effective Modeling","created":"2023-11-01","cited_by_count":1,"area":null},{"id":"2311.12902","title":"Enhancing Solutions for Complex PDEs: Introducing Complementary Convolution and Equivariant Attention in Fourier Neural Operators","created":"2023-11-21","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":"2408.02275","title":"Geometric Algebra Meets Large Language Models: Instruction-Based Transformations of Separate Meshes in 3D, Interactive and Controllable Scenes","created":"2024-08-05","cited_by_count":1,"area":null},{"id":"2409.14660","title":"Fourier neural operators for spatiotemporal dynamics in two-dimensional turbulence","created":"2024-09-23","cited_by_count":1,"area":null},{"id":"2510.23215","title":"Accelerating Eigenvalue Dataset Generation via Chebyshev Subspace Filter","created":"2025-10-27","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":"2606.29584","title":"Geometric Algebra Meets Cartesian Tensors: Higher-Order Equivariance for Interatomic Potentials","created":"2026-06-28","cited_by_count":1,"area":null},{"id":"2402.11568","title":"A novel Fourier neural operator framework for classification of multi-sized images: Application to three dimensional digital porous media","created":"2024-02-18","cited_by_count":0,"area":null},{"id":"2407.09926","title":"Metric Learning for Clifford Group Equivariant Neural Networks","created":"2024-07-13","cited_by_count":0,"area":null},{"id":"2408.13619","title":"STAResNet: a Network in Spacetime Algebra to solve Maxwell's PDEs","created":"2024-08-24","cited_by_count":0,"area":null},{"id":"2505.11766","title":"Reformulating Neural Operators in $d+1$ Dimensions for Embedding Evolution","created":"2025-05-17","cited_by_count":0,"area":null},{"id":"2507.01040","title":"Fast Clifford Neural Layers","created":"2025-06-22","cited_by_count":0,"area":null},{"id":"2509.12151","title":"Learning Contact Dynamics through Touching: Action-conditional Graph Neural Networks for Robotic Peg Insertion","created":"2025-09-15","cited_by_count":0,"area":null},{"id":"2510.03290","title":"Single-Core Superscalar Optimization of Clifford Neural Layers","created":"2025-09-29","cited_by_count":0,"area":null},{"id":"2510.14007","title":"Conditional Clifford-Steerable CNNs for PDE Modeling","created":"2025-10-15","cited_by_count":0,"area":null},{"id":"2510.23221","title":"Accelerating IC Thermal Simulation Data Generation via Block Krylov and Operator Action","created":"2025-10-27","cited_by_count":0,"area":null},{"id":"2601.07191","title":"On Lie Groups Preserving Subspaces of Degenerate Clifford Algebras","created":"2026-01-12","cited_by_count":0,"area":null},{"id":"2602.05977","title":"Clifford Kolmogorov-Arnold Networks","created":"2026-02-05","cited_by_count":0,"area":null},{"id":"2604.09321","title":"UHD Low-Light Image Enhancement via Real-Time Enhancement Methods with Clifford Information Fusion","created":"2026-04-10","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":"2605.18606","title":"PACE-FNO: Physics-Aligned Canonical Equivariance for Fourier Neural Operators","created":"2026-05-18","cited_by_count":0,"area":null},{"id":"2605.18816","title":"When Does Equivariance Help? Canonical Alignment in Neural Fluid Surrogates","created":"2026-05-12","cited_by_count":0,"area":null},{"id":"2606.23044","title":"Prime Fourier Embeddings: A Principled Basis for Modular Arithmetic","created":"2026-06-22","cited_by_count":0,"area":null},{"id":"2607.06634","title":"When Do Geometric Algebra Layers Beat Scalarization? A Controlled Study on SO(3)-Equivariant Vector Laws","created":"2026-07-07","cited_by_count":0,"area":null},{"id":"2610.01322","title":"Clifford Sheaf Neural Networks","created":"2026-10-01","cited_by_count":0,"area":null},{"id":"2610.12776","title":"Coordinate-Free Automatic Differentiation","created":"2026-10-08","cited_by_count":0,"area":null}],"next_cursor":null}