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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/2y7c3/super-resolution-neural-operator

공식 코드

Super-Resolution Neural Operator

https://github.com/Axect/HyperbolicLR

공식 코드

HyperbolicLR: Epoch insensitive learning rate scheduler

https://github.com/Axect/Neural_Hamilton

공식 코드

Neural Hamilton: Can A.I. Understand Hamiltonian Mechanics?

https://github.com/BaratiLab/OFormer

공식 코드

Transformer for Partial Differential Equations' Operator Learning

https://github.com/CSML-IIT-UCL/kooplearn

공식 코드

Learning dynamical systems: an example from open quantum system dynamics

https://github.com/Cadence-Celsius/DeepOHeat

공식 코드

DeepOHeat: Operator Learning-based Ultra-fast Thermal Simulation in 3D-IC Design

https://github.com/EdoardoCalvello/TransformerNeuralOperators

공식 코드

Continuum Attention for Neural Operators

https://github.com/HamidrezaEiv/EquiNO

공식 코드

EquiNO: A Physics-Informed Neural Operator for Multiscale Simulations

https://github.com/HewlettPackard/separable-operator-networks

공식 코드

Separable Operator Networks

https://github.com/Learning-and-Intelligent-Systems/predicators_behavior

공식 코드

Learning Efficient Abstract Planning Models that Choose What to Predict

https://github.com/LiuYangMage/in-context-operator-networks/tree/main/icon

공식 코드

In-Context Operator Learning with Data Prompts for Differential Equation Problems

https://github.com/Machine-Learning-Dynamical-Systems/kooplearn

공식 코드

Sharp Spectral Rates for Koopman Operator Learning

https://github.com/Rayzhangzirui/BILO

공식 코드

BiLO: Bilevel Local Operator Learning for PDE inverse problems

https://github.com/aarentai/Ultrasound-Tfno-MIDL

공식 코드

Neural Operator Learning for Ultrasound Tomography Inversion

https://github.com/aims-madison/neural-dynamical-operator

공식 코드

Neural Dynamical Operator: Continuous Spatial-Temporal Model with Gradient-Based and Derivative-Free Optimization Methods

https://github.com/alperelli/MCAOL

공식 코드

Multi-Channel Convolutional Analysis Operator Learning for Dual-Energy CT Reconstruction

https://github.com/amazon-science/boon

공식 코드

Guiding continuous operator learning through Physics-based boundary constraints

https://github.com/amir-cardiolab/xai_fda

공식 코드

Interpreting and generalizing deep learning in physics-based problems with functional linear models

https://github.com/anthonyzhou-1/hamiltonian_pdes

공식 코드

Neural Functional: Learning Function to Scalar Maps for Neural PDE Surrogates

https://github.com/ashiq24/uno

공식 코드

U-NO: U-shaped Neural Operators

https://github.com/bayesianaigroup/ifno

공식 코드

Invertible Fourier Neural Operators for Tackling Both Forward and Inverse Problems

https://github.com/bogdanraonic3/convolutionalneuraloperator

공식 코드

Convolutional Neural Operators for robust and accurate learning of PDEs

https://github.com/bookdiver/scoreoperator

공식 코드

Infinite-dimensional Diffusion Bridge Simulation via Operator Learning

https://github.com/bostanabad-research-group/gp-for-operator-learning

공식 코드

Operator Learning with Gaussian Processes

https://github.com/bravedrxutf/vol

공식 코드

Variational operator learning: A unified paradigm marrying training neural operators and solving partial differential equations

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

공식 코드

Geometry Aware Operator Transformer as an Efficient and Accurate Neural Surrogate for PDEs on Arbitrary Domains

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

공식 코드

Generative AI for fast and accurate statistical computation of fluids

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

공식 코드

Poseidon: Efficient Foundation Models for PDEs

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

공식 코드

RIGNO: A Graph-based framework for robust and accurate operator learning for PDEs on arbitrary domains

https://github.com/ccr-cheng/infgcn-pytorch

공식 코드

Equivariant Neural Operator Learning with Graphon Convolution

https://github.com/chaow-mat/general_integral_transform_neural_network

공식 코드

GIT-Net: Generalized Integral Transform for Operator Learning

https://github.com/chuanqichencc/continous-spatial-temporal-model

공식 코드

Neural Dynamical Operator: Continuous Spatial-Temporal Model with Gradient-Based and Derivative-Free Optimization Methods

https://github.com/dahoas/edm

공식 코드

DFU: scale-robust diffusion model for zero-shot super-resolution image generation

https://github.com/delta-lab-ai/data_efficient_nopt

공식 코드

Data-Efficient Operator Learning via Unsupervised Pretraining and In-Context Learning

https://github.com/devzhk/PINO

공식 코드

Physics-Informed Neural Operator for Learning Partial Differential Equations

https://github.com/devzhk/dsno-pytorch

공식 코드

Fast Sampling of Diffusion Models via Operator Learning

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