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Chapter 40: Topics, Concepts, and AI Methods Discussed in Chapters

    https://doi.org/10.1142/9789811265679_0040Cited by:0 (Source: Crossref)
    Abstract:

    The following sections are included:

    • Overview

    • Chapter 3. Data-Driven Science in the Era of AI: From Patterns to Practice

    • Chapter 4. AI in the Broader Context of Data Science

    • Chapter 5. AlphaFold — The End of the Protein Folding Problem or the Start of Something Bigger?

    • Chapter 6. Applications of AI in Astronomy

    • Chapter 7. Machine Learning for Complex Instrument Design and Optimization

    • Chapter 8. Artificial Intelligence (AI) and Machine Learning (ML) at Experimental Facilities

    • Chapter 9. The First Exascale Supercomputer: Accelerating AI-for-Science and Beyond

    • Chapter 10. Benchmarking for AI for Science

    • Chapter 11. Radio Astronomy and the Square Kilometer Array

    • Chapter 12. The Rise of the Machines

    • Chapter 13. AI for Net-Zero

    • Chapter 14. AI for Climate Science

    • Chapter 15. Accelerating Fusion Energy with AI

    • Chapter 16. Artificial Intelligence for a Resilient and Flexible Power Grid

    • Chapter 17. AI and Machine Learning in Observing Earth from Space

    • Chapter 18. Artificial Intelligence in Plant and Agricultural Research

    • Chapter 19. AI and Pathology: Steering Treatment and Predicting Outcomes

    • Chapter 20. The Role of Artificial Intelligence in Epidemiological Modeling

    • Chapter 21. Big AI: Blending Big Data with Big Theory to Build Virtual Humans

    • Chapter 22. A Roadmap for Defining Machine Learning Standards in Life Sciences

    • Chapter 23. Artificial Intelligence for Materials

    • Chapter 24. Artificial Intelligence for Accelerating Materials Discovery

    • Chapter 25. Experimental Particle Physics and Artificial Intelligence

    • Chapter 26. AI and Theoretical Particle Physics

    • Chapter 27. Schema.org for Scientific Data

    • Chapter 28. AI-Enabled HPC Workflows

    • Chapter 29. AI for Scientific Visualization

    • Chapter 30. Uncertainty Quantification in AI for Science

    • Chapter 31. AI for Next-Generation Global Network-Integrated Systems and Testbeds

    • Chapter 32. AI for Optimal Experimental Design and Decision-Making

    • Chapter 33. FAIR: Making Data AI-Ready

    • Chapter 34. Large Language Models for Science

    • Chapter 35. Autonomous Vehicles

    • Chapter 36. The Automated AI-Driven Future of Scientific Discovery

    • Chapter 37. Towards Reflection Competencies in Intelligent Systems for Science

    • Chapter 38. The Interface of Machine Learning and Causal Inference