Oromion's Projects
Finite Element tools in Julia: Heat diffusion application
Compute the coefficients of explicit or implicit finite difference schemes
This is the repository of the Finite Element Method course in National Agrarian University - La Molina.
The finite element method (FEM) is a numerical technique for finding approximate solutions to boundary value problems for partial differential equations. It uses subdivision of a whole problem domain into simpler parts, called finite elements, and variational methods from the calculus of variations to solve the problem by minimizing an associated error function. Analogous to the idea that connecting many tiny straight lines can approximate a larger circle, FEM encompasses methods for connecting many simple element equations over many small subdomains, named finite elements, to approximate a more complex equation over a larger domain.
Implementation of Firefly Algorithm in Python
Curso de Física I
A general purpose numerical simulator supporting nested dynamical systems and a convenient macro-based data logger.
A flow chart creator written in C++ using the Qt Graphics View Framework.
HTML fluid simulation using Emscripten, C++, cmake.
22.901 Fortran Class
A highly customizable and improved version of Carmine Spagnuolo's Twenty Seconds Curriculum Vitae.
Forward Mode Automatic Differentiation for Julia
A jupyter notebook with some stuff on the FT
Use Fourier transform to learn operators in differential equations.
In the last few years, I have developed a lot of Jupyter notebooks to draw fractals. In this repository, I will gradually share them. Creative Commons Attribution-ShareAlike 4.0 International License.
:books: Freely available programming books
FreeFEM source code
Frontend Workshop from HTML/CSS/JS to TypeScript/React/Redux
手把手撕LeetCode题目，扒各种算法套路的裤子，not only how，but also why. English version supported!
Getting up to speed with Python: a book for self-learners
Cheetsheet zur Funktionalanalysis Vorlesung des Wintersemesters 2019/2020 am KIT
The purpose of this repo is to make it easy to get started with JAX, Flax, and Haiku. It contains my "Machine Learning with JAX" series of tutorials (YouTube videos and Jupyter Notebooks) as well as the content I found useful while learning about the JAX ecosystem.
ggplot themes and scales
Git hooks for LaTeX
Arch Linux package for GitHub Actions remote runner.
Udacity ocurse
Full workspace template for GitPod. Provides extensions and tools for CI students.
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React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent