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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/nickhnelsen/fourier-neural-mappings

공식 코드

An operator learning perspective on parameter-to-observable maps

https://github.com/nickhnelsen/random-features-banach

공식 코드

Operator Learning Using Random Features: A Tool for Scientific Computing

https://github.com/nicolas-guerra/learning-where-to-learn

공식 코드

Learning Where to Learn: Training Distribution Selection for Provable OOD Performance

https://github.com/nvidia/torch-harmonics

공식 코드

Spherical Fourier Neural Operators: Learning Stable Dynamics on the Sphere

https://github.com/optray/piano

공식 코드

Deciphering and integrating invariants for neural operator learning with various physical mechanisms

https://github.com/pietronvll/encoderops

공식 코드

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems

https://github.com/predictiveintelligencelab/cvit

공식 코드

CViT: Continuous Vision Transformer for Operator Learning

https://github.com/predictiveintelligencelab/improveddeeponets

공식 코드

Improved architectures and training algorithms for deep operator networks

https://github.com/predictiveintelligencelab/loca

공식 코드

Learning Operators with Coupled Attention

https://github.com/predictiveintelligencelab/pde-constrained-optimization-pi-deeponet

공식 코드

Fast PDE-constrained optimization via self-supervised operator learning

https://github.com/qkoopman/QuACK

공식 코드

QuACK: Accelerating Gradient-Based Quantum Optimization with Koopman Operator Learning

https://github.com/quest-lab-iisc/pinto

공식 코드

A physics-informed transformer neural operator for learning generalized solutions of initial boundary value problems

https://github.com/rasoulnajafi/microstructure-embedded-autoencoder-mea-

공식 코드

Introducing a microstructure-embedded autoencoder approach for reconstructing high-resolution solution field data from a reduced parametric space

https://github.com/rezanajian/fol

공식 코드

Finite Operator Learning: Bridging Neural Operators and Numerical Methods for Efficient Parametric Solution and Optimization of PDEs

https://github.com/rsmath/ensemble-deeponet

공식 코드

Ensemble and Mixture-of-Experts DeepONets For Operator Learning

https://github.com/scaomath/galerkin-transformer

공식 코드

Choose a Transformer: Fourier or Galerkin

https://github.com/scaomath/torch-cfd

공식 코드

Spectral-Refiner: Accurate Fine-Tuning of Spatiotemporal Fourier Neural Operator for Turbulent Flows

https://github.com/shashanksule/esvd

공식 코드

On the limits of neural network explainability via descrambling

https://github.com/shib0li/mra-fno

공식 코드

Multi-Resolution Active Learning of Fourier Neural Operators

https://github.com/sponproject/spon

공식 코드

Structure-Preserving Operator Learning

https://github.com/stc-lab/on-traffic

공식 코드

ON-Traffic: An Operator Learning Framework for Online Traffic Flow Estimation and Uncertainty Quantification from Lagrangian Sensors

https://github.com/stfc-sciml/zerocoordinateshift

공식 코드

Zero Coordinate Shift: Whetted Automatic Differentiation for Physics-informed Operator Learning

https://github.com/thu-ml/NUNO

공식 코드

NUNO: A General Framework for Learning Parametric PDEs with Non-Uniform Data

https://github.com/thu-ml/gnot

공식 코드

GNOT: A General Neural Operator Transformer for Operator Learning

https://github.com/tyler-ingebrand/operatorfunctionencoder

공식 코드

Basis-to-Basis Operator Learning Using Function Encoders

https://github.com/unique-subedi/active-operator-learning

공식 코드

On the Benefits of Active Data Collection in Operator Learning

https://github.com/varunsingh88/MT-DeepONet

공식 코드

Synergistic Learning with Multi-Task DeepONet for Efficient PDE Problem Solving

https://github.com/vduruiss/sropnet

공식 코드

An Operator Learning Framework for Spatiotemporal Super-resolution of Scientific Simulations

https://github.com/vwurstep/adanns

공식 코드

Algorithmically Designed Artificial Neural Networks (ADANNs): Higher order deep operator learning for parametric partial differential equations

https://github.com/yashpatel5400/schrodinger_op

공식 코드

Operator Learning for Schrödinger Equation: Unitarity, Error Bounds, and Time Generalization

https://github.com/yilei-wu/imbalanced-regression

공식 코드

Improve Representation for Imbalanced Regression through Geometric Constraints

https://github.com/yizheng-wang/research-on-solving-partial-differential-equations-of-solid-mechanics-based-on-pinn

공식 코드

DCEM: A deep complementary energy method for solid mechanics

https://github.com/yzshi5/mino

공식 코드

Mesh-Informed Neural Operator : A Transformer Generative Approach

https://github.com/zhaozhiwhy/diffeomorphism-neural-operator

공식 코드

Diffeomorphism Neural Operator for various domains and parameters of partial differential equations

https://github.com/zhijie-group/orthogonal-neural-operator

공식 코드

Improved Operator Learning by Orthogonal Attention

https://github.com/zhuhanqing/PACE-Light

공식 코드

PACE: Pacing Operator Learning to Accurate Optical Field Simulation for Complicated Photonic Devices

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