import Yongqi.Li as YL
def myself():
major = "MSc Applied Computational Science and Engineering"
platforms = {"Windows", "Ubuntu", "MacOS"}
languages = {
"frontend": ["Markdown", "Latex"],
"backend": ["Python", "C++","Java"],
"database": "SQL",
"ML Framework":["sklearn","pytorch"],
}
strategies ={"Inversion", "Optimization", "Parallelization"}
ides = {"Pycharm", "VSCode", "Visual Studio", "Eclipse",}
tools = {"Git", "GitHub", "SQLite", "Unity","VMWare"}
likes = {"maths","network", "AI", "physics"}
if __name__ == "__main__":
myself()- 🧠 Firedrake and Pytorch Project: Participated in the development of a framework coupling the machine learning library PyTorch and the PDE system Firedrake and studied goal oriented mesh adaptive refinement based on Riemannian metric.
- 💡 Solve NS Equation in Parallel: Model a Newtonian Fluid in a tube and simulate it with cpp; code parallelization implemented with MPI
- 🌏 A virtual solar system and Armageddon forcast: Use classical numerical methods like Euler, Rk4 and lagrange interpolation to simulate real life problems in Astrophysics
- 🔍Detecting impact craters using DL techniques: Automatically detecting impact craters in images of planetary surfaces (Mars and Moon) and deriving from this a crater-size frequency distribution that can be used for dating and a GAN to generate more image data.
**