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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
Spectral-Refiner: Accurate Fine-Tuning of Spatiotemporal Fourier Neural Operator for Turbulent FlowsCode2
CViT: Continuous Vision Transformer for Operator LearningCode2
Equivariant Graph Neural Operator for Modeling 3D DynamicsCode2
PDE Generalization of In-Context Operator Networks: A Study on 1D Scalar Nonlinear Conservation LawsCode2
Spherical Fourier Neural Operators: Learning Stable Dynamics on the SphereCode2
Convolutional Neural Operators for robust and accurate learning of PDEsCode2
Neural Operator: Learning Maps Between Function SpacesCode2
Mesh-Informed Neural Operator : A Transformer Generative ApproachCode1
Principled Approaches for Extending Neural Architectures to Function Spaces for Operator LearningCode1
A Physics-Informed Meta-Learning Framework for the Continuous Solution of Parametric PDEs on Arbitrary GeometriesCode1
Improve Representation for Imbalanced Regression through Geometric ConstraintsCode1
RIGNO: A Graph-based framework for robust and accurate operator learning for PDEs on arbitrary domainsCode1
Point-DeepONet: A Deep Operator Network Integrating PointNet for Nonlinear Analysis of Non-Parametric 3D Geometries and Load ConditionsCode1
A physics-informed transformer neural operator for learning generalized solutions of initial boundary value problemsCode1
VICON: Vision In-Context Operator Networks for Multi-Physics Fluid Dynamics PredictionCode1
PACE: Pacing Operator Learning to Accurate Optical Field Simulation for Complicated Photonic DevicesCode1
Neural Hamilton: Can A.I. Understand Hamiltonian Mechanics?Code1
Basis-to-Basis Operator Learning Using Function EncodersCode1
Generative AI for fast and accurate statistical computation of fluidsCode1
PROSE-FD: A Multimodal PDE Foundation Model for Learning Multiple Operators for Forecasting Fluid DynamicsCode1
Operator Learning with Gaussian ProcessesCode1
Operator Learning Using Random Features: A Tool for Scientific ComputingCode1
Separable Operator NetworksCode1
Finite Operator Learning: Bridging Neural Operators and Numerical Methods for Efficient Parametric Solution and Optimization of PDEsCode1
Continuum Attention for Neural OperatorsCode1
Positional Knowledge is All You Need: Position-induced Transformer (PiT) for Operator LearningCode1
Towards a Foundation Model for Partial Differential Equations: Multi-Operator Learning and ExtrapolationCode1
A finite operator learning technique for mapping the elastic properties of microstructures to their mechanical deformationsCode1
Deep Learning Computed Tomography based on the Defrise and Clack AlgorithmCode1
Data-Efficient Operator Learning via Unsupervised Pretraining and In-Context LearningCode1
Diffeomorphism Neural Operator for various domains and parameters of partial differential equationsCode1
An operator learning perspective on parameter-to-observable mapsCode1
Deciphering and integrating invariants for neural operator learning with various physical mechanismsCode1
Neural Dynamical Operator: Continuous Spatial-Temporal Model with Gradient-Based and Derivative-Free Optimization MethodsCode1
Equivariant Neural Operator Learning with Graphon ConvolutionCode1
Improved Operator Learning by Orthogonal AttentionCode1
Learning to Predict Structural VibrationsCode1
Fine-Tune Language Models as Multi-Modal Differential Equation SolversCode1
Operator Learning with Neural Fields: Tackling PDEs on General GeometriesCode1
An enrichment approach for enhancing the expressivity of neural operators with applications to seismologyCode1
NUNO: A General Framework for Learning Parametric PDEs with Non-Uniform DataCode1
Kernel Methods are Competitive for Operator LearningCode1
In-Context Operator Learning with Data Prompts for Differential Equation ProblemsCode1
GNOT: A General Neural Operator Transformer for Operator LearningCode1
DeepOHeat: Operator Learning-based Ultra-fast Thermal Simulation in 3D-IC DesignCode1
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