paper
2023
Sub-seasonal to seasonal
Explainability, uncertainty & physics New model or method
Physics-informed / physics-constrainedSeasonal to annual Preprint
2023-03-13
008
Mar 2023
Biao Chen, Huadong Du, Kang Pu et al.
Research Square
Physics-informed neural networks predict regional ENSO variables and aim to add physical interpretability; the abstract is unavailable, so results cannot be verified.
Physics-informed neural networks for prediction and physical interpretability analysis of key regional variables of ENSO
In one sentence. Physics-informed neural networks predict regional ENSO variables and aim to add physical interpretability; the abstract is unavailable, so results cannot be verified.
Labels
- Topic
- Sub-seasonal to seasonal, Explainability, uncertainty & physics
- Type
- New model or method
- Method
- Physics-informed / physics-constrained
- Time scale
- Seasonal to annual
- Domain
- Global
- Data
- ERA5 / reanalysis
- Status
- Research
- Identifiers
- DOI:10.21203/rs.3.rs-2624566/v1 OpenAlex:W4324088793
- Added
- 2026-10-22
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