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Found 3 papers, 1 papers with code
TPTNet: A Data-Driven Temperature Prediction Model Based on Turbulent Potential Temperature
The resulting turbulent potential temperature data at irregularly distributed stations were used as input for predicting the turbulent potential temperature at forecast hours through three trained networks based on convolutional neural network (CNN), Swin Transformer, and a graphic neural network (GNN).
Deep Particulate Matter Forecasting Model Using Correntropy-Induced Loss
Forecasting the particulate matter (PM) concentration in South Korea has become urgently necessary owing to its strong negative impact on human life.
Deep unsupervised learning of turbulence for inflow generation at various Reynolds numbers
In the present work, we applied generative adversarial networks (GANs), a representative of unsupervised learning, to generate an inlet boundary condition of turbulent channel flow.
Fluid Dynamics Computational Physics
