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PDE Surrogate Modeling

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코드

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

https://github.com/BaratiLab/FactFormer

공식 코드

Scalable Transformer for PDE Surrogate Modeling

https://github.com/bogdanraonic3/convolutionalneuraloperator

공식 코드

Convolutional Neural Operators for robust and accurate learning of PDEs

https://github.com/dlanzo/CRANE

공식 코드

Extreme time extrapolation capabilities and thermodynamic consistency of physics-inspired Neural Networks for the 3D microstructure evolution of materials via Cahn-Hilliard flow

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/maxxxzdn/erwin

공식 코드

Erwin: A Tree-based Hierarchical Transformer for Large-scale Physical Systems

https://github.com/microsoft/pdearena

공식 코드

Towards Multi-spatiotemporal-scale Generalized PDE Modeling

https://github.com/nec-research/cape-ml4sci

공식 코드

Learning Neural PDE Solvers with Parameter-Guided Channel Attention

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

공식 코드

Aero-Nef: Neural Fields for Rapid Aircraft Aerodynamics Simulations

https://github.com/britacatalin/bsa

Erwin: A Tree-based Hierarchical Transformer for Large-scale Physical Systems

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

Convolutional Neural Operators for robust and accurate learning of PDEs

https://github.com/pdearena/pdearena

Towards Multi-spatiotemporal-scale Generalized PDE Modeling

https://github.com/pdebench/pdebench

Learning Neural PDE Solvers with Parameter-Guided Channel Attention

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