petropoliszhang's Stars
ruzgol/US-Stock-Index-Forcasting-using-RNN
leriomaggio/deep-learning-keras-tensorflow
Introduction to Deep Neural Networks with Keras and Tensorflow
evolbio/FitODE
Fit ODE and NODE models to data using Julia DiffEqFlux.jl
Joe-Hall-Lee/Mathematical-Modeling-Algorithms-and-Applications
《数学建模算法与应用》（第3版）系列讲座（2022年）。
Tongshiyan/TsyMatlab
PRML/PRMLT
Matlab code of machine learning algorithms in book PRML
DarioArzaba/MATLABScripts
MATLAB scripts used in circuit design and numerical analysis
Mechanics-Mechatronics-and-Robotics/Physics-based-loss-and-machine-learning-approach-in-application-to-viscous-fluids-flow-modeling
The idea of taking the path of least resistance arose a long time ago, and people find its confirmation both in themselves and in the environment. Aristotle expressed this idea in his writings, Fermat used this idea to describe the law of refraction of light, and Maupertuis was the first to formulate the principle of least action in mechanics. The variational approach of finding the extremum of an objective functional is an alternative approach to the solution of partial differential equations in mechanics of continua. The great challenge in variational calculus direct methods is to find a set of functions that will be able to approximate the solution accurately enough. Artificial neural networks are powerful tools for approximation, and the physics-based functional can be the natural loss for a machine learning method. In this paper, we focus on the loss that may take non-linear fluid properties and mass forces into account. We modified the energy-based variational principle and determined the constraints on its unknown functions that implement boundary conditions. We explored artificial neural networks as an option for loss minimization and the approximation of the unknown function. We compared the obtained results with the known solutions. The proposed method allows modeling non-Newtonian fluids flow including blood, synthetic oils, paints, plastic, bulk materials, and even rheomagnetic fluids.
MJfadeaway/DAS
DAS: A deep adaptive sampling method for solving high-dimensional partial differential equations
levimcclenny/SA-PINNs
Implementation of the paper "Self-Adaptive Physics-Informed Neural Networks using a Soft Attention Mechanism" [AAAI-MLPS 2021]
rsmath/dt-pinn
Accelerating Physics Informed Neural Networks (PINNs) using Meshless Discretizations
lobster87/Concentrating-solar-power-Heliostat-field-design
This Project looks to design and optimize a Heliostat field for Concentrating Solar Power
PredictiveIntelligenceLab/GradientPathologiesPINNs
ehsankharazmi/hp-VPINNs
hp-VPINNs: variational physics-informed neural network with domain decomposition is a general framework to solve differential equations
ippqw5/PINNLearning
Implement PINN with high level APIs of TF2.0, including a solution of coupled PDEs with PINN
farscape-project/PINNs_Benchmark
Physics-Informed Neural Networks designed to solve the Two-Dimensional Wave Equation in both TensorFlow and PyTorch. Code is designed to benchmark the performance of PINNs across various hardware architectures.
Vay-keen/Machine-learning-learning-notes
周志华《机器学习》又称西瓜书是一本较为全面的书籍，书中详细介绍了机器学习领域不同类型的算法(例如：监督学习、无监督学习、半监督学习、强化学习、集成降维、特征选择等)，记录了本人在学习过程中的理解思路与扩展知识点，希望对新人阅读西瓜书有所帮助！
google-research/data-driven-advection
google/data-driven-discretization-1d
Code for "Learning data-driven discretizations for partial differential equations"
maziarraissi/PINNs
Physics Informed Deep Learning: Data-driven Solutions and Discovery of Nonlinear Partial Differential Equations
rtqichen/torchdiffeq
Differentiable ODE solvers with full GPU support and O(1)-memory backpropagation.
xinychen/latex-cookbook
LaTeX论文写作教程 (清华大学出版社)
luwill/Machine_Learning_Code_Implementation
Mathematical derivation and pure Python code implementation of machine learning algorithms.
tiagosalvador/quadtrees-mesh
Code for the paper "Higher-order Adaptive Finite Difference Methods for Fully Nonlinear Elliptic Equations" by Brittany Froese Hamfeldt and Tiago Salvador. (https://doi.org/10.1007/s10915-017-0586-5)
heucoder/dimensionality_reduction_alo_codes
特征提取/数据降维:PCA、LDA、MDS、LLE、TSNE等降维算法的python实现
koulakis/dynamical-systems
Code for the book 'Dynamical systems with applications using Python'.
Inhenn/Fractional-Black-Scholes-PDE-Solution
MinglangYin/PyTorchTutorial
Examplary code for NN, MFNN, DynNet, PINNs and CNN
zergtant/pytorch-handbook
pytorch handbook是一本开源的书籍，目标是帮助那些希望和使用PyTorch进行深度学习开发和研究的朋友快速入门，其中包含的Pytorch教程全部通过测试保证可以成功运行
ShusenTang/Dive-into-DL-PyTorch
本项目将《动手学深度学习》(Dive into Deep Learning)原书中的MXNet实现改为PyTorch实现。