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Operator learning

Learn an operator between infinite dimensional Hilbert spaces or Banach spaces

Papers

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Showing 1–50 of 347 papers

TitleStatusHype
A Library for Learning Neural OperatorsCode7
Neural Operators with Localized Integral and Differential KernelsCode4
Guaranteed Approximation Bounds for Mixed-Precision Neural OperatorsCode4
Poseidon: Efficient Foundation Models for PDEsCode3
Geometry Aware Operator Transformer as an Efficient and Accurate Neural Surrogate for PDEs on Arbitrary DomainsCode2
Spherical Fourier Neural Operators: Learning Stable Dynamics on the SphereCode2
Convolutional Neural Operators for robust and accurate learning of PDEsCode2
Equivariant Graph Neural Operator for Modeling 3D DynamicsCode2
PDE Generalization of In-Context Operator Networks: A Study on 1D Scalar Nonlinear Conservation LawsCode2
Neural Operator: Learning Maps Between Function SpacesCode2
Spectral-Refiner: Accurate Fine-Tuning of Spatiotemporal Fourier Neural Operator for Turbulent FlowsCode2
CViT: Continuous Vision Transformer for Operator LearningCode2
Operator Learning Using Random Features: A Tool for Scientific ComputingCode1
NUNO: A General Framework for Learning Parametric PDEs with Non-Uniform DataCode1
Operator Learning with Gaussian ProcessesCode1
Multiwavelet-based Operator Learning for Differential EquationsCode1
Learning Efficient Abstract Planning Models that Choose What to PredictCode1
Neural Hamilton: Can A.I. Understand Hamiltonian Mechanics?Code1
Operator Learning with Neural Fields: Tackling PDEs on General GeometriesCode1
Guiding continuous operator learning through Physics-based boundary constraintsCode1
GNOT: A General Neural Operator Transformer for Operator LearningCode1
Improved architectures and training algorithms for deep operator networksCode1
Sharp Spectral Rates for Koopman Operator LearningCode1
Learning Operators with Coupled AttentionCode1
Learning Symbolic Operators for Task and Motion PlanningCode1
Mesh-Informed Neural Operator : A Transformer Generative ApproachCode1
Fast Sampling of Diffusion Models via Operator LearningCode1
Equivariant Neural Operator Learning with Graphon ConvolutionCode1
Neural Dynamical Operator: Continuous Spatial-Temporal Model with Gradient-Based and Derivative-Free Optimization MethodsCode1
An operator learning perspective on parameter-to-observable mapsCode1
Finite Operator Learning: Bridging Neural Operators and Numerical Methods for Efficient Parametric Solution and Optimization of PDEsCode1
Improved Operator Learning by Orthogonal AttentionCode1
DeepOHeat: Operator Learning-based Ultra-fast Thermal Simulation in 3D-IC DesignCode1
A Physics-Informed Meta-Learning Framework for the Continuous Solution of Parametric PDEs on Arbitrary GeometriesCode1
DCEM: A deep complementary energy method for solid mechanicsCode1
Deciphering and integrating invariants for neural operator learning with various physical mechanismsCode1
A physics-informed transformer neural operator for learning generalized solutions of initial boundary value problemsCode1
Deep Learning Computed Tomography based on the Defrise and Clack AlgorithmCode1
Adaptive Fourier Neural Operators: Efficient Token Mixers for TransformersCode1
Deep transfer operator learning for partial differential equations under conditional shiftCode1
Fast PDE-constrained optimization via self-supervised operator learningCode1
Basis-to-Basis Operator Learning Using Function EncodersCode1
Generative AI for fast and accurate statistical computation of fluidsCode1
Continuum Attention for Neural OperatorsCode1
Principled Approaches for Extending Neural Architectures to Function Spaces for Operator LearningCode1
An enrichment approach for enhancing the expressivity of neural operators with applications to seismologyCode1
In-Context Operator Learning with Data Prompts for Differential Equation ProblemsCode1
Kernel Methods are Competitive for Operator LearningCode1
Learning dynamical systems: an example from open quantum system dynamicsCode1
A finite operator learning technique for mapping the elastic properties of microstructures to their mechanical deformationsCode1
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