ComputationalPhysics2 and ComputationalPhysics300
These two tools are competitors, as both are educational courses teaching computational physics, with "CompPhysics/ComputationalPhysics2" offering an advanced course with an emphasis on computational quantum mechanics and machine learning, while "MaterSim/ComputationalPhysics300" is a general computational physics class taught at UNLV.
About ComputationalPhysics2
CompPhysics/ComputationalPhysics2
Advanced course in Computational Physics, see texbook at http://compphysics.github.io/ComputationalPhysics2/doc/LectureNotes/_build/html/ with an emphasis on computational quantum mechanics, machine learning and quantum computing.
This project provides resources for scientists, engineers, and graduate students who need to simulate the behavior of complex quantum-mechanical systems with many interacting particles. It offers lecture materials, code, and exercises to help users understand and apply advanced computational methods like quantum Monte Carlo and mean-field theories. The goal is to perform large-scale simulations that generate new insights into quantum systems across various scientific and engineering disciplines.
About ComputationalPhysics300
MaterSim/ComputationalPhysics300
computational physics class taught at UNLV (Phys300)
This course material provides an introduction to applying computational methods for solving physics problems. It takes students from foundational Python programming concepts to advanced topics like Fourier transforms, Monte Carlo simulations, optimization, and machine learning. Undergraduate physics students interested in scientific computing and data analysis would use these materials.
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