Path Catalog
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Path Catalog
列表数据实时取自 GET /api/v1/paths，仅包含存在已发布版本的 Path。
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This learning path builds a solid foundation in the mathematics essential for scientific computing, covering basic algebra, linear algebra, calculus, differential equations, probability, and numerical analysis. It progresses from fundamental algebraic concepts to more advanced topics, ensuring that each new area is supported by necessary prerequisites. Designed for high school STEM students, the path emphasizes practical understanding and application to scientific problems.
This learning path introduces high school students to the discipline of scientific computing. It covers the definition, history, core applications, essential tools, and the fundamental workflow, with a gentle introduction to programming and the mathematical prerequisites needed to get started.
A comprehensive graduate-level learning path for aspiring computational science researchers, covering the full research lifecycle from scientific reasoning and literature review to computational experimentation, data analysis, reproducibility, communication, ethics, and peer review.
This path equips advanced computational science students with the knowledge to apply quantum machine learning to scientific problems. It covers quantum computing fundamentals, core machine learning, variational quantum algorithms, QML models, and hands-on applications, emphasizing the practical integration of these fields.
This path provides HPC researchers with a comprehensive understanding of exascale computing, covering architectural trends, programming models, performance and energy challenges, and key application domains. Learners will explore the technical hurdles and opportunities presented by exascale systems, preparing them for advanced research and development.
This learning path introduces graduate students in computational science to neuromorphic computing, covering spiking neural networks, neuromorphic hardware, and their applications in scientific domains. It builds from foundational concepts in computational neuroscience and machine learning to advanced topics in hardware and scientific workflows.
This learning path provides a comprehensive introduction to digital twin technology, covering foundational concepts, enabling technologies, and practical applications. It guides learners from basic principles to advanced predictive modeling and domain-specific implementations, emphasizing the integration of simulation, real-time data, and machine learning.
A graduate-level learning path for computational science researchers to understand and apply AI methods to scientific discovery. It covers machine learning foundations, deep learning for science, generative models, reinforcement learning, AI-driven experimentation, and self-driving labs, emphasizing real-world applications and research frontiers.
This learning path equips computational scientists with essential communication skills, covering technical writing, presentations, data visualization, data storytelling, and interdisciplinary collaboration. It progresses from foundational principles to practical applications, enabling effective communication of complex computational research.
This learning path equips researchers with practical open science methods for computational work, covering open access publishing, open data management, open source software, preprints, and transparent peer review. It emphasizes reproducible workflows and effective research communication, guiding learners from foundational principles to advanced application.