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Guided learning journeys that build knowledge step by step.
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A comprehensive graduate-level path from quantum mechanics foundations through quantum algorithms, simulation, and error correction, tailored for physics students with programming skills.
A comprehensive graduate-level path covering the mathematical foundations, numerical techniques, and practical implementations needed to solve Einstein's equations and model black holes and gravitational waves. It progresses from tensor calculus and numerical methods through the 3+1 decomposition, numerical relativity formulations, and advanced topics like binary black hole mergers and gravitational wave extraction.
A comprehensive graduate-level path covering lattice gauge theory, from quantum field theory prerequisites and lattice discretization to Monte Carlo methods, continuum limit, and applications in lattice QCD. Learners will develop both conceptual understanding and practical computational skills.
This path guides advanced students through the theory and practice of computational crystal structure prediction (CSP). Starting from fundamentals of DFT and energy landscapes, it covers global optimization methods including genetic algorithms and random structure search, and concludes with practical applications and validation. The path emphasizes the conceptual foundations and algorithmic strategies used to explore energy landscapes and predict stable crystal structures.
This path equips HPC researchers with a deep understanding of exascale computing, covering architectures, programming models, applications, and performance challenges. It builds from foundational HPC concepts to advanced exascale-specific topics, emphasizing practical implications for scientific computing.
This graduate-level learning path systematically develops the mathematical and computational foundations required to master advanced quantum many-body methods, including Green's functions, quantum Monte Carlo, configuration interaction, coupled cluster, and tensor networks. It begins with essential quantum mechanics and numerical techniques, progresses through mean-field theory and second quantization, and culminates in advanced many-body frameworks and their practical applications.
This advanced learning path equips physics students with the knowledge and skills to understand and apply computational methods in astrophysics. It covers essential numerical techniques, N-body simulations, hydrodynamics, radiative transfer, and cosmological simulations, culminating in the application to star formation.
This advanced learning path equips physics students with the computational skills needed to analyze high-energy physics data. It covers the underlying particle physics theory, essential programming in Python and C++, Monte Carlo methods, event and detector simulation, and data analysis techniques including machine learning and statistical inference.
A comprehensive learning path for physics students to master computational methods for condensed matter systems, covering electronic structure (DFT), phonons, defects, and magnetism, with a strong foundation in solid state physics and numerical methods.
This learning path equips physics and engineering students with the knowledge and skills to model acoustic wave propagation using computational methods. It covers the governing equations, numerical techniques (FDTD, FEM), and practical applications in room acoustics, emphasizing the underlying physics and mathematical foundations.