YangFaye99

YangFaye99's Stars

• FaspDevTeam/faspsolver

  The FASP package is designed for developing and testing new efficient solvers and preconditioners for discrete partial differential equations (PDEs) or systems of PDEs. The main components of the package are standard Krylov methods, algebraic multigrid methods, and incomplete factorization methods. Based on these standard techniques, we build efficient solvers, based on the framework of Auxiliary Space Preconditioning, for several complicated applications.

  Language:C3210

• steffi7574/LayerParallelLearning

  Language:C++101

• XuFengthucs/fSVT

  fast randomized SVD and its application to SVT algorithm

  Language:MATLAB238

• WenjianYu/randQB_auto

  randomized QB factorization for fixed-precision low-rank matrix approximation

  Language:MATLAB133

• Schweinepriester/github-profile-achievements

  A collection listing all Achievements available on the GitHub profile 🏆

  Language:Markdown4.7k446

• singgel/Study-Floder

  相当不错的图书，例如《数学之美》、《浪潮之巅》、《TCP/IP卷一/卷二/卷三》等；一些大的上传受限制的文件《图解TCP_IP_第5版》、《深入理解Java虚拟机JVM》、《effective java》等在README

  2.8k926

• scaomath/galerkin-transformer

  [NeurIPS 2021] Galerkin Transformer: a linear attention without softmax for Partial Differential Equations

  Language:Python21928

• remcovandermeer/Optimally-Weighted-PINNs

  Language:Python3820

• juliusberner/deep_kolmogorov

  Numerically Solving Parametric Families of High-Dimensional Kolmogorov Partial Differential Equations via Deep Learning (NeurIPS 2020)

  Language:Python219

• maziarraissi/FBSNNs

  Forward-Backward Stochastic Neural Networks: Deep Learning of High-dimensional Partial Differential Equations

  Language:Python14273

• Photon-AI-Research/NeuralSolvers

  Neural network based solvers for partial differential equations and inverse problems :milky_way:. Implementation of physics-informed neural networks in pytorch.

  Language:Python13946

• YichengDWu/NeuralGraphPDE.jl

  Integrating Neural Ordinary Differential Equations, the Method of Lines, and Graph Neural Networks

  Language:Julia171

• adityabalu/DiffNet

  DiffNet: A FEM based neural PDE solver package

  Language:Jupyter Notebook118

• Duke-MG-Lab/Allen-Cahn-FNO

  Fourier Neural Operators to solve for Allen Cahn PDE equations

  Language:Python175

• jayroxis/qres

  Learning with Higher Expressive Power than Neural Networks (On Learning PDEs)

  Language:Jupyter Notebook156

• brandstetter-johannes/MP-Neural-PDE-Solvers

  Repo to the paper "Message Passing Neural PDE Solvers"

  Language:Python12024

• pooyasf/DGM

  Solving High Dimensional Partial Differential Equations with Deep Neural Networks

  Language:Python3011

• crispitagorico/torchspde

  Neural Stochastic PDEs: resolution-invariant modelling of continuous spatiotemporal dynamics

  Language:Jupyter Notebook468

• isds-neu/PhyCRNet

  Physics-informed convolutional-recurrent neural networks for solving spatiotemporal PDEs

  Language:Python11734

• cics-nd/ar-pde-cnn

  Physics-constrained auto-regressive convolutional neural networks for dynamical PDEs

  Language:Python8430

• SciML/NeuralPDE.jl

  Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation

  Language:Julia978197

• Zymrael/awesome-neural-ode

  A collection of resources regarding the interplay between differential equations, deep learning, dynamical systems, control and numerical methods.

  1.3k150

• google-research/torchsde

  Differentiable SDE solvers with GPU support and efficient sensitivity analysis.

  Language:Python1.6k197

• martenlienen/finite-element-networks

  Reference implementation of Finite Element Networks as proposed in "Learning the Dynamics of Physical Systems from Sparse Observations with Finite Element Networks" at ICLR 2022

  Language:Jupyter Notebook676

• neuraloperator/graph-pde

  Using graph network to solve PDEs

  Language:Python33282

• neuraloperator/neuraloperator

  Learning in infinite dimension with neural operators.

  Language:Python2k538