Quantum and AI for Climate, Earth, and Environmental Simulations - Brian McCut

Jul 8, 2024

Quantum and AI for Climate, Earth, and Environmental Simulations

Presenter: Brian McCut, Principal R&D Engineer

  • Affiliation: Naval Nuclear Lab (NNL)
  • Focus: Emerging digital technology for US Naval nuclear propulsion
    • Roles: Leads quantum tech integration and future tech development in supercomputing and AI.

Project Overview

  • Context: Quantum and AI in Earth system modeling and climate simulations.
  • Nature: Open-ended and research-focused project suitable for aspiring researchers.

Importance of Earth System Simulations

  • Complexity: Highly complex physics (e.g., formation of water droplets, ice crystals, interaction of light).
  • Scale: From atomic to global scale; nonlinear, coupled equations.
  • Computational Needs: Requires massive supercomputing resources.
  • Relevance: Future energy and environment challenges demand intensive modeling, simulation, and data analysis.

Potential of Quantum and AI

  • Benefits:
    • Faster results
    • Increased accuracy
    • New types of calculations
  • Research Goals:
    • Survey current literature and state-of-the-art techniques.
    • Identify areas for extending current research.

Project Focus Areas

  • 1. Simulation Improvements:
    • Weather, climate, and various Earth systems.
    • Use AI/Quantum to accelerate and improve simulations.
  • 2. Data Analysis:
    • Apply machine learning on climate/weather datasets.
    • Predictive analysis on large open-source data sets.
  • 3. Sustainable Technologies:
    • Energy materials, batteries, photovoltaics, power grid, agriculture, fertilizers.
    • Applications of quantum and AI in these domains.

Key Project Questions

  • Problem Importance: Why is the chosen problem significant?
  • Current Solutions: How is the problem being tackled today?
  • Quantum/AI Benefits: How can these technologies improve current methods?
  • Implementation Strategy: How to develop a practical application or prototype?

Learning Goals

  • Research Navigation: Develop skills in finding and evaluating research.
  • Understanding Simulations: Learn how simulations and ML are currently practiced.
  • Quantum/AI Impact: Determine the practical benefits and limitations of these technologies.
  • Application Development: Translate ideas and equations into code.

Example Topics for Exploration

  1. Climate Data Models:
    • Use AI to create fast models of Earth's climate from historical data.
  2. Quantum Algorithms:
    • Propose quantum methods for simulating batteries or photovoltaic materials.
  3. Light and Clouds:
    • Model light interaction with water droplets and clouds using quantum/AI.
  4. Quantum Machine Learning:
    • Compare quantum and classical ML in identifying severe weather events.
  5. Energy Comparisons:
    • Analyze energy consumption of different quantum hardware types.

Tips for Successful Research

  • Be Bold and Curious: Explore the topic deeply and think creatively.
  • Be Creative: Push research boundaries and extend existing work.
  • Be Yourself: Don’t rely on generative AI for solutions; use it to understand complex topics.
  • Have Fun: Enjoy the research process and the learning experience.

Closing Remarks and Resources

  • Encouragement to explore and have fun with the project.
  • Offer for additional resources and guidance if needed.

Thank you and looking forward to your findings!