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hxyan's Projects

rocalphago icon rocalphago

An independent, student-led replication of DeepMind's 2016 Nature publication, "Mastering the game of Go with deep neural networks and tree search" (Nature 529, 484-489, 28 Jan 2016), details of which can be found on their website https://deepmind.com/publications.html.

segment-anything icon segment-anything

The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.

sfm_learn icon sfm_learn

一个基于OpenCV的SFM简单实现,学习用途。欢迎光临我的主页查看更多其他项目详情。

splatam icon splatam

SplaTAM: Splat, Track & Map 3D Gaussians for Dense RGB-D SLAM

statistic-study-notes icon statistic-study-notes

李航统计学习方法(第二版)的学习笔记,包括:1、每章重点公式的手动推导 2、每章算法的Python自实现 3、学习过程中的笔记与心得 4、每章节的课后习题 5、每周都会按照至少一周一章的进度定时将自己的学习进度更新到这个仓库

su2 icon su2

SU2: An Open-Source Suite for Multiphysics Simulation and Design

sugar icon sugar

Official PyTorch implementation of SuGaR: Surface-Aligned Gaussian Splatting for Efficient 3D Mesh Reconstruction and High-Quality Mesh Rendering (CVPR 2024)

suitesparse-metis-for-windows icon suitesparse-metis-for-windows

CMake scripts for painless usage of SuiteSparse+METIS from Visual Studio and the rest of Windows/Linux/OSX IDEs supported by CMake

superpoint icon superpoint

Efficient neural feature detector and descriptor

swe-gnn-paper-repository- icon swe-gnn-paper-repository-

Code repository for paper "Rapid Spatio-Temporal Flood Modelling via Hydraulics-Based Graph Neural Networks"

swmm5plus icon swmm5plus

Prototype Fortran 2008 engine for the EPA Storm Water Management Model (SWMM)

taskflow icon taskflow

A General-purpose Parallel and Heterogeneous Task Programming System

telemac icon telemac

R package as interface for the hydrodynamic model TELEMAC-2D

tgpt-pinn icon tgpt-pinn

Transformed Generative Pre-Trained Physics-Informed Neural Networks (TGPT-PINN), a framework that extends Physics-Informed Neural Networks (PINNs) and reduced basis methods (RBM) to the non- linear model reduction regime while maintaining the type of network structure and the unsupervised nature of its learning.

ttt-lm-pytorch icon ttt-lm-pytorch

Official PyTorch implementation of Learning to (Learn at Test Time): RNNs with Expressive Hidden States

ugks icon ugks

[Forked from @lainme] Codes for solving gas flow problems based on the Unified Gas-Kinetic Scheme

wenoext icon wenoext

Weighted essentially non-oscillatory library for the framework of OpenFOAM

wntr icon wntr

An EPANET compatible python package to simulate and analyze water distribution networks under disaster scenarios.

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