Towards Hybrid Physics-Machine Learning Parameterizations: Employing Data Assimilation For Online Learning Of Turbulence And Convection Closures In A Unified Scheme
- - Classical And Modern Physics
- - Computer And It: Science And Engineering
- - Construction And Civil Engineering
- - Earth, Geography And Geological Sciences
- - Ecology And Environmental Science
- - Educational Learning, Academic Policy And Curriculcum Development
- - Educational Learning, Policy And Curriculcum Development
- - Electronic And Communication Technologies & Power Engineering
- - Finance, Business And Economics
- - Food, Health And Nutrition Studies
- - Governance, Politics And Policy Studies
- - History, Heritage And Archaeology
- - Integrative Life Sciences
- - Language Science: Literary & Linguistic Studies
- - Law
- - Law And Forensics
- - Mechanical And Industrial Systems Engineering
- - Media Studies And Journalism
- - Medical Research And Practice
- - Pharmacology And Pharmaceutical Sciences
- - Philosophy: Ethics And Humanities
- - Plant Biology And Agriculture
- - Psychological Sciences
- - Pure And Applied Chemistry
- - Pure And Applied Mathematics
- - Sociology, Culture, And Society
Book Details :
Towards Hybrid Physics-Machine Learning Parameterizations: Employing Data Assimilation For Online Learning Of Turbulence And Convection Closures In A Unified Scheme