Machine Learning for Ocean Closures - Advances and Lessons - Laure Zanna - Climate-C21

Machine Learning for Ocean Closures - Advances and Lessons - Laure Zanna - Climate-C21

Kavli Institute for Theoretical Physics via YouTube Direct link

Predictions vs Truth

17 of 31

17 of 31

Predictions vs Truth

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Machine Learning for Ocean Closures - Advances and Lessons - Laure Zanna - Climate-C21

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  1. 1 Intro
  2. 2 Climate Dynamicism
  3. 3 Grid Size
  4. 4 Mathematical Models
  5. 5 Current Climate Models
  6. 6 Why are we here
  7. 7 What are we doing
  8. 8 Ocean Model
  9. 9 Finding a Filter
  10. 10 Methods
  11. 11 Learning Subgrid Closures
  12. 12 Learning Turbulent Closures
  13. 13 The Good News
  14. 14 Retraining the Model
  15. 15 Strong Extremes
  16. 16 Boundary Conditions
  17. 17 Predictions vs Truth
  18. 18 Missing forcing
  19. 19 Field of view
  20. 20 Online Model
  21. 21 Stochastic Parameters
  22. 22 Global Kinetic Energy
  23. 23 Learning probabilistically
  24. 24 Learning equations
  25. 25 Learning new physics
  26. 26 Kinetic energy
  27. 27 Summary
  28. 28 Questions
  29. 29 Zoom
  30. 30 Vertical Fluxes
  31. 31 Decomposers

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