Data-Driven and Data-Assisted Modeling for Applications in Fluid Dynamics and Geophysics
Kavli Institute for Theoretical Physics via YouTube
Overview
Syllabus
Introduction
Modeling a fluid dynamical system
Outline
Datadriven ML models
Dynamics models
Predicting chaotic dynamical systems
High resolution forecasting
Results
Why Machine Learning
Dataassisted forecasting
Hybrid model
Computational cost
Machine learning
Fluid modeling vs image processing
Hybrid architecture
High resolution trajectory
Initial prototype
High resolution spectrum
RMS error curves
Visual results
Hybrid numerical weather prediction
Preprint
Conclusion
Questions
Technical questions
Real data assimilation
MLPD Hybrid
Amount of data
Multitime step optimization
Taught by
Kavli Institute for Theoretical Physics