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Operator learning

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Learn an operator between infinite dimensional Hilbert spaces or Banach spaces

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/zongyi-li/fourier_neural_operator

공식 코드

Neural Operator: Learning Maps Between Function Spaces

https://gitlab.com/dustin_lee/neural-operators

공식 코드

Loss Terms and Operator Forms of Koopman Autoencoders

https://gitlab.isae-supaero.fr/gi.catalani/aero-nepf

공식 코드

Aero-Nef: Neural Fields for Rapid Aircraft Aerodynamics Simulations

https://gitlab.lrz.de/KoopmanLearning/KoopmanizingFlows

공식 코드

Diffeomorphically Learning Stable Koopman Operators

https://github.com/2JaeYong/FEONet_project

Error analysis for finite element operator learning methods for solving parametric second-order elliptic PDEs

https://github.com/ChengzeYe/Defrise-and-Clack-reconstruction

Deep Learning Computed Tomography based on the Defrise and Clack Algorithm

https://github.com/Hedawl/PINO-MBD

Physics-Informed Neural Operator for Learning Partial Differential Equations

https://github.com/PaddlePaddle/PaddleScience

LNO: Laplace Neural Operator for Solving Differential Equations

https://github.com/UnofficialJuliaMirror/ConvolutionalOperatorLearning.jl-3c17ac3d-2343-4d75-b01f-81723beeda4b

Convolutional Analysis Operator Learning: Acceleration and Convergence

https://github.com/UnofficialJuliaMirrorSnapshots/ConvolutionalOperatorLearning.jl-3c17ac3d-2343-4d75-b01f-81723beeda4b

Convolutional Analysis Operator Learning: Acceleration and Convergence

https://github.com/bernatj/ai-based-attribution

Spherical Fourier Neural Operators: Learning Stable Dynamics on the Sphere

https://github.com/camlab-ethz/convolutionalneuraloperator

Convolutional Neural Operators for robust and accurate learning of PDEs

https://github.com/dahong67/ConvolutionalAnalysisOperatorLearning.jl

Convolutional Analysis Operator Learning: Acceleration and Convergence

https://github.com/dahong67/ConvolutionalOperatorLearning.jl

Convolutional Analysis Operator Learning: Acceleration and Convergence

https://github.com/fishmoon1234/ifno-tissue

A Physics-Guided Neural Operator Learning Approach to Model Biological Tissues from Digital Image Correlation Measurements

https://github.com/giovannicatalani/enf2enf_pytorch

Geometry aware inference of steady state PDEs using Equivariant Neural Fields representations

https://github.com/haozhongkai/gnot

GNOT: A General Neural Operator Transformer for Operator Learning

https://github.com/neural-operator/pino

Physics-Informed Neural Operator for Learning Partial Differential Equations

https://github.com/neuraloperator/physics_informed

Physics-Informed Neural Operator for Learning Partial Differential Equations

https://github.com/nvidia/earth2mip

Spherical Fourier Neural Operators: Learning Stable Dynamics on the Sphere

https://github.com/nvlabs/afno-transformer

Adaptive Fourier Neural Operators: Efficient Token Mixers for Transformers

https://github.com/oisin-m/gfn

GFN: A graph feedforward network for resolution-invariant reduced operator learning in multifidelity applications

https://github.com/ronuchit/LOFT_IROS_2021

Learning Symbolic Operators for Task and Motion Planning

https://github.com/shawnrosofsky/pino_applications

Physics-Informed Neural Operator for Learning Partial Differential Equations

https://github.com/wenhaoding/pino-cde

Physics-Informed Neural Operator for Learning Partial Differential Equations

https://github.com/xlyu0127/DeepOHeat-v1

DeepOHeat-v1: Efficient Operator Learning for Fast and Trustworthy Thermal Simulation and Optimization in 3D-IC Design

https://github.com/yzshi5/spl_ofm

Neural Operators with Localized Integral and Differential Kernels

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.