paper
2019
Hybrid physics-ML & parameterization
New model or method
Physics-informed / physics-constrainedClimate projections / centennialLawrence Berkeley National Laboratory / NERSC Journal
2019-01-09
014
Jan 2019
Karthik Kashinath
99th American Meteorological Society Annual Meeting
Lawrence Berkeley National Laboratory / NERSC
Physics-informed generative learning is proposed to emulate unresolved climate-model physics; the abstract is missing, so details are limited.
Hybrid physics-ML & parameterization
Physics-Informed Generative Learning to Emulate Unresolved Physics in Climate Models
In one sentence. Physics-informed generative learning is proposed to emulate unresolved climate-model physics; the abstract is missing, so details are limited.
Labels
- Topic
- Hybrid physics-ML & parameterization
- Type
- New model or method
- Method
- Physics-informed / physics-constrained
- Time scale
- Climate projections / centennial
- Domain
- Global
- Data
- Other
- Status
- Research
- Identifiers
- OpenAlex:W3004404188
- Added
- 2026-10-22
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