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Guided learning journeys that build knowledge step by step.
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A systematic learning path for physics students to understand and apply computational methods in optics, covering wave and geometric optics, diffraction, beam propagation, and optical system modeling. It builds from fundamental optical theory and numerical methods to advanced simulation techniques.
This learning path guides physics students from essential statistical mechanics and classical mechanics through the algorithms, force fields, ensembles, and analysis techniques of molecular dynamics, culminating in applications to condensed matter systems. It emphasizes the conceptual prerequisites and practical programming skills needed to understand and run MD simulations.
A systematic learning path covering the mathematical foundations of fluid dynamics, numerical discretization techniques, and advanced topics such as turbulence modeling and compressible flow. Designed for physics and engineering students to understand and apply CFD methods.
A systematic learning path for physics students to understand and implement computational methods for statistical physics, focusing on Monte Carlo methods, statistical ensembles, the Ising model, phase transitions, and critical phenomena. The path builds from foundational statistical mechanics and programming skills to advanced simulation techniques and data analysis.
A comprehensive learning path for physics students to understand and apply numerical methods for solving quantum mechanical problems. It covers the necessary quantum mechanics background, linear algebra, numerical techniques, and practical implementation for eigenvalue problems, variational methods, and basis sets.
This learning path provides a systematic introduction to computational methods for electrodynamics, covering the theoretical foundations of Maxwell's equations, numerical techniques such as FDTD and finite element methods, and applications to scattering, wave propagation, and antenna design. It is designed for university physics students with a background in electromagnetism and numerical methods.
This learning path guides physics students through the essential computational techniques for solving classical mechanics problems, from Newtonian dynamics to Hamiltonian systems and chaotic behavior. It covers numerical integrators, phase space analysis, and many-body simulations, with hands-on programming exercises in Python.
A beginner-friendly path for high school physics students to learn essential computational tools used in physics research and coursework. Covers the command line, version control with Git, Python basics, Jupyter notebooks, and key scientific Python packages, with a final project applying these skills to a physics problem.
A beginner-friendly path for physics students to learn Python programming and apply it to physics problems. Covers Python basics, numerical computation with NumPy, data visualization with Matplotlib, and data analysis, culminating in a simple physics simulation.
A structured learning path covering the core mathematical tools needed for computational physics: vector calculus, complex analysis, differential equations, special functions, and linear algebra. This path emphasizes the conceptual foundations and practical applications, preparing physics students to formulate and solve problems numerically.