Pinned Repositories
baswam95.github.io
official sites
CGNS
The CFD General Notation System (CGNS) provides a standard for recording and recovering computer data associated with the numerical solution of fluid dynamics equations. All development work and bug fixes should be based off the 'develop' branch, CGNS uses the branching model Gitflow. https://cgnsorg.atlassian.net is used for issue tracking.
FAST_LIO
A computationally efficient and robust LiDAR-inertial odometry (LIO) package
KernelNeuralOptimalTransport
PyTorch implementation of "Kernel Neural Optimal Transport" (ICLR 2023)
LSSTC-DSFP-Sessions
Lecture slides, Jupyter notebooks, and other material from the LSSTC Data Science Fellowship Program
NLA-2022
Skoltech-Numerical Linear Algebra (NLA) Course Edition 2022
OpenFOAM_Tutorials_
OpenFOAM Tutorials!
Pressure-Driven-Pipe-Flow
Hagen-Poiseuille Flow
SUAVE
An Aircraft Design Toolbox
eocsi-hackathon-2022
Notebooks and materials for the EOCSI Hackathon 2022, https://frontiersi.com.au/climate-innovation-hack/
baswam95's Repositories
baswam95/SUAVE
An Aircraft Design Toolbox
baswam95/baswam95.github.io
official sites
baswam95/OpenFOAM_Tutorials_
OpenFOAM Tutorials!
baswam95/CGNS
The CFD General Notation System (CGNS) provides a standard for recording and recovering computer data associated with the numerical solution of fluid dynamics equations. All development work and bug fixes should be based off the 'develop' branch, CGNS uses the branching model Gitflow. https://cgnsorg.atlassian.net is used for issue tracking.
baswam95/FAST_LIO
A computationally efficient and robust LiDAR-inertial odometry (LIO) package
baswam95/KernelNeuralOptimalTransport
PyTorch implementation of "Kernel Neural Optimal Transport" (ICLR 2023)
baswam95/LSSTC-DSFP-Sessions
Lecture slides, Jupyter notebooks, and other material from the LSSTC Data Science Fellowship Program
baswam95/NLA-2022
Skoltech-Numerical Linear Algebra (NLA) Course Edition 2022
baswam95/notebooks
Programming Exercises
baswam95/numpy-stl
Simple library to make working with STL files (and 3D objects in general) fast and easy.
baswam95/Pressure-Driven-Pipe-Flow
Hagen-Poiseuille Flow
baswam95/Titanic-Machine-Learning-from-Disaster
Start here if... You're new to data science and machine learning, or looking for a simple intro to the Kaggle prediction competitions. Competition Description The sinking of the RMS Titanic is one of the most infamous shipwrecks in history. On April 15, 1912, during her maiden voyage, the Titanic sank after colliding with an iceberg, killing 1502 out of 2224 passengers and crew. This sensational tragedy shocked the international community and led to better safety regulations for ships. One of the reasons that the shipwreck led to such loss of life was that there were not enough lifeboats for the passengers and crew. Although there was some element of luck involved in surviving the sinking, some groups of people were more likely to survive than others, such as women, children, and the upper-class. In this challenge, we ask you to complete the analysis of what sorts of people were likely to survive. In particular, we ask you to apply the tools of machine learning to predict which passengers survived the tragedy. Practice Skills Binary classification Python and R basics