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Classroom Contents
Data-Driven and Data-Assisted Modeling for Applications in Fluid Dynamics and Geophysics
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- 1 Introduction
- 2 Modeling a fluid dynamical system
- 3 Outline
- 4 Datadriven ML models
- 5 Dynamics models
- 6 Predicting chaotic dynamical systems
- 7 High resolution forecasting
- 8 Results
- 9 Why Machine Learning
- 10 Dataassisted forecasting
- 11 Hybrid model
- 12 Computational cost
- 13 Machine learning
- 14 Fluid modeling vs image processing
- 15 Hybrid architecture
- 16 High resolution trajectory
- 17 Initial prototype
- 18 High resolution spectrum
- 19 RMS error curves
- 20 Visual results
- 21 Hybrid numerical weather prediction
- 22 Preprint
- 23 Conclusion
- 24 Questions
- 25 Technical questions
- 26 Real data assimilation
- 27 MLPD Hybrid
- 28 Amount of data
- 29 Multitime step optimization
