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Guided learning journeys that build knowledge step by step.
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A comprehensive graduate-level learning path for aspiring researchers in computational physics, covering numerical methods, algorithm validation, reproducibility, literature review, scientific communication, and ethics. The path is designed to build systematic research skills from foundational mathematics and programming through to advanced research practices.
This advanced graduate-level path guides physics students from the fundamentals of quantum mechanics and quantum computing through the principles of quantum simulation, covering both analog and digital approaches, key applications, and the current landscape of quantum advantage. Learners will develop a conceptual and practical understanding of how quantum systems can simulate other quantum systems, culminating in an analysis of near-term applications and the quest for quantum advantage.
This path guides physics students from foundational machine learning and numerical methods to a working understanding of physics-informed neural networks (PINNs). It covers the PINN formulation, applications to forward ODE/PDE problems, inverse problem solving, and physics-constrained learning, with practical implementation insights.
This path guides physics researchers from foundational HPC concepts to the specialized knowledge required for exascale computing. It covers exascale architectures, performance optimization, scalable physics applications, and data management, culminating in a capstone project that integrates these skills.
This advanced graduate-level learning path equips physics students with the theoretical foundations and computational techniques required to model cosmic structure formation, analyze the cosmic microwave background, and constrain dark matter and dark energy. It progresses from core cosmological concepts and numerical methods to modern simulation and data analysis workflows.
This path guides physics graduate students from foundational quantum computing and machine learning concepts to advanced quantum machine learning (QML) techniques, with a focus on physics applications. It covers quantum data encoding, variational quantum algorithms, quantum neural networks, and their use in solving physical problems.
This learning path prepares physics educators to design and deliver effective computational physics instruction. It covers the essential content knowledge, pedagogical foundations, curriculum design, interactive tools, and assessment strategies needed to teach computational physics in a professional setting.
This path equips physics researchers with the skills to develop and optimize parallel scientific applications for modern HPC systems. It covers HPC architectures, parallel programming with MPI and OpenMP, performance analysis, and practical physics applications.
This learning path equips physics researchers with essential communication skills tailored to computational physics, covering technical writing, data visualization, presentations, and interdisciplinary collaboration. It builds from foundational scientific communication to advanced practices for publishing and presenting computational research.
A professional learning path for physics researchers to integrate open science practices into computational research. It covers open data, open-source software, reproducible workflows, collaboration, and publishing, with a focus on practical application in computational physics.