hungrycat233 Goto Github PK

100-days-of-ml-code

100-Days-Of-ML-Code中文版

2d_hidden_elasticity_pinn

adaptive-selfsupervision-pinns

Gradient-based adaptive sampling algorithms for self-supervising PINNs

amaw-pinn

Enhancing PINNs for Solving PDEs via Adaptive Collocation Point Movement and Adaptive Loss Weighting

aqualord

Data for Water Resource Management

atl-pinn

Auxiliary-Tasks Learning for Physics-Informed Neural Network-Based Partial Differential Equations Solving

atmospheric_dispersion_pinn

atpinn

bayesian-neural-networks

Pytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace, SG-HMC and more

bc-pinn

A Backward Compatible -- Physics Informed Neural Network for Allen Cahn and Cahn Hilliard Equations

bep-thesis

Python code for BEP thesis

bias-adjustment-python

Methods for climate data bias correction/adjustment

bias-correction-for-climate-models

Python execution of bias correction methods for climate models using MBC package from Cannon AJ (2018) doi:10.1007/s00382-017-3580-6, Cannon AJ (2016) doi: 10.1175/JCLI-D-15-0679.1, and Cannon AJ, Sobie SR, Murdock TQ (2015) doi:10.1175/JCLI-D-14-00754.1.

bias_correction

python library for bias correction

bilstm_crf

用序列标注方法将文本中的人名、地名和组织机构名识别出来。使用BiLSTM-CRF模型，pytorch框架。

control-pinns

cpinn

Competitive Physics Informed Networks

crest-imap

coupled hydrologic and hydrodynamic modeling framework

dash-stock-price-prediction

Visualising stock price predictions for several Machine Learning models using DASH, Python

dataassi-transport

deep-backward-heat-conduction-problem

deepstefan

deepxde

A library for scientific machine learning and physics-informed learning

dyffusion

[NeurIPS 2023] A Dynamics-informed Diffusion Model for Spatiotemporal Forecasting

ensemble-learning

机器学习算法之集成学习

ensemble_learning

集成学习方法

ep-pinns

EP-PINNs implementation for 1D and 2D forward and inverse solvers for the Aliev-Panfilov cardiac electrophysiology model. Also includes Matlab finite-differences solver for data generation.

fbpinns

Solve forward and inverse problems related to partial differential equations using finite basis physics-informed neural networks (FBPINNs)

federated-learning

A PyTorch Implementation of Federated Learning http://doi.org/10.5281/zenodo.4321561

fmpinn

Solving a class of elliptic partial differential equations(PDEs) with multiple scales utilizing Fourier-based mixed physics informed neural networks(dubbed FMPINN), the solver of FMPINN is configured as a multi-scale deep neural networks.