Digital twin
Manufaqtury запустила цифровой двойник бизнес-центра в платформе «Призма»
В платформе для управления коммерческой недвижимостью «Призма» (Prysm) появился цифровой двойник здания. Он связывает...
Physics-Informed Neural Networks for Climate-Aware Digital Twins
This study evaluates a physics-informed neural network for potential-temperature forecasting under incomplete thermal observations, constrained by a pressure-coordinate advection-source equation and a diabatic-source closure frozen after the preceding 12 hours. Validation uses hourly ERA5 reanalysis at three pressure levels for one-, two- and three-hour horizons against persistence, local-trend, and two neural baselines.
Why it matters
The approach shows how physical constraints complement data in short-horizon forecasting, which is relevant for climate-aware digital twins of cities and regions.
Relevant to DT products
- Original headline
- Sparse-Observation Atmospheric Thermal Forecasting with Physics-Informed Neural Networks for Climate-Aware Digital Twins
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