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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/diffeqml/kairos

공식 코드

Transform Once: Efficient Operator Learning in Frequency Domain

https://github.com/ecker-lab/FQ-Operator

공식 코드

Learning to Predict Structural Vibrations

https://github.com/ecker-lab/learning_vibrating_plates

공식 코드

Learning to Predict Structural Vibrations

https://github.com/ehsanhaghighat/en-deeponet

공식 코드

An enrichment approach for enhancing the expressivity of neural operators with applications to seismology

https://github.com/ehsanhaghighat/x-deeponet

공식 코드

An enrichment approach for enhancing the expressivity of neural operators with applications to seismology

https://github.com/emazap7/leray_schauder_neural_net

공식 코드

Leray-Schauder Mappings for Operator Learning

https://github.com/eydcao/vicon

공식 코드

VICON: Vision In-Context Operator Networks for Multi-Physics Fluid Dynamics Prediction

https://github.com/felix-lyx/prose

공식 코드

Towards a Foundation Model for Partial Differential Equations: Multi-Operator Learning and Extrapolation

https://github.com/gaurav71531/mwt-operator

공식 코드

Multiwavelet-based Operator Learning for Differential Equations

https://github.com/giovannicatalani/enf2enf

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

https://github.com/hamidrezaeiv/kpca-deeponet

공식 코드

Nonlinear model reduction for operator learning

https://github.com/hippylib/hippyflow

공식 코드

Derivative-informed neural operator acceleration of geometric MCMC for infinite-dimensional Bayesian inverse problems

https://github.com/jangseop-park/point-deeponet

공식 코드

Point-DeepONet: A Deep Operator Network Integrating PointNet for Nonlinear Analysis of Non-Parametric 3D Geometries and Load Conditions

https://github.com/jingminsun/lemon_prose

공식 코드

LeMON: Learning to Learn Multi-Operator Networks

https://github.com/jingminsun/prose_v1

공식 코드

Time-Series Forecasting, Knowledge Distillation, and Refinement within a Multimodal PDE Foundation Model

https://github.com/jingzhang-jz/no_hyperbolic_delay

공식 코드

Neural Operators for PDE Backstepping Control of First-Order Hyperbolic PIDE with Recycle and Delay

https://github.com/joshuaxiao98/cmwno

공식 코드

Coupled Multiwavelet Neural Operator Learning for Coupled Partial Differential Equations

https://github.com/junfeng-chen/position_induced_transformer

공식 코드

Positional Knowledge is All You Need: Position-induced Transformer (PiT) for Operator Learning

https://github.com/katiana22/surrogate-overparameterization

공식 코드

On the influence of over-parameterization in manifold based surrogates and deep neural operators

https://github.com/katiana22/tl-deeponet

공식 코드

Deep transfer operator learning for partial differential equations under conditional shift

https://github.com/kazizzad/LabelShiftEstimator

공식 코드

Importance Weight Estimation and Generalization in Domain Adaptation under Label Shift

https://github.com/koflera/convsparsitynns

공식 코드

Convolutional Analysis Operator Learning by End-To-End Training of Iterative Neural Networks

https://github.com/lcsl/nys-koop-lqr

공식 코드

Linear quadratic control of nonlinear systems with Koopman operator learning and the Nyström method

https://github.com/liaochunyang/RandomFeatureOperatorLearning

공식 코드

Cauchy Random Features for Operator Learning in Sobolev Space

https://github.com/liuyangmage/in-context-operator-networks

공식 코드

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

https://github.com/louisserrano/coral

공식 코드

Operator Learning with Neural Fields: Tackling PDEs on General Geometries

https://github.com/lugroupumn/icl-ellipticpdes

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In-Context Learning of Linear Systems: Generalization Theory and Applications to Operator Learning

https://github.com/lugroupumn/icl_linear_systems

공식 코드

In-Context Learning of Linear Systems: Generalization Theory and Applications to Operator Learning

https://github.com/lukebhan/neuraloperatorsforgainkernels

공식 코드

Neural Operators of Backstepping Controller and Observer Gain Functions for Reaction-Diffusion PDEs

https://github.com/matthieudarcy/kernelsoperatorlearning

공식 코드

Kernel Methods are Competitive for Operator Learning

https://github.com/mechatoz/convolt

공식 코드

Convolutional Analysis Operator Learning: Acceleration and Convergence

https://github.com/minkaixu/egno

공식 코드

Equivariant Graph Neural Operator for Modeling 3D Dynamics

https://github.com/mr-siddy/DPHGNN

공식 코드

DPHGNN: A Dual Perspective Hypergraph Neural Networks

https://github.com/nboulle/operatorlearningadjoint

공식 코드

Operator learning without the adjoint

https://github.com/neuraloperator/neuraloperator

공식 코드

Guaranteed Approximation Bounds for Mixed-Precision Neural Operators

https://github.com/neuraloperator/nns-to-nos

공식 코드

Principled Approaches for Extending Neural Architectures to Function Spaces for Operator Learning

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