PINN-Cast combines continuous-depth Neural ODEs inside transformer blocks with a two-branch attention module and physics-informed loss to produce short-term weather forecasts that respect governing physical principles.
arXiv preprint arXiv:2311.07222 (20 24)
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AI methods can strengthen cross-domain interactions and support more coherent multi-component representations in Earth system models.
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PINN-Cast: Exploring the Role of Continuous-Depth NODE in Transformers and Physics Informed Loss as Soft Physical Constraints in Short-term Weather Forecasting
PINN-Cast combines continuous-depth Neural ODEs inside transformer blocks with a two-branch attention module and physics-informed loss to produce short-term weather forecasts that respect governing physical principles.
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Toward Artificial Intelligence Enabled Earth System Coupling
AI methods can strengthen cross-domain interactions and support more coherent multi-component representations in Earth system models.