A 3D super-resolution of wind fields via physics-informed pixel-wise self-attention generative adversarial network
This work addresses the need for efficient monitoring of greenhouse gas sources at high spatial resolution to mitigate global warming, representing an incremental improvement in domain-specific simulation methods.
The study tackled the problem of computationally expensive high-resolution 3D wind field simulations by developing a physics-informed super-resolution GAN that upscales low-resolution wind fields by 9 times, reducing computational cost by 89.7 times while maintaining high-fidelity results.
To mitigate global warming, greenhouse gas sources need to be resolved at a high spatial resolution and monitored in time to ensure the reduction and ultimately elimination of the pollution source. However, the complexity of computation in resolving high-resolution wind fields left the simulations impractical to test different time lengths and model configurations. This study presents a preliminary development of a physics-informed super-resolution (SR) generative adversarial network (GAN) that super-resolves the three-dimensional (3D) low-resolution wind fields by upscaling x9 times. We develop a pixel-wise self-attention (PWA) module that learns 3D weather dynamics via a self-attention computation followed by a 2D convolution. We also employ a loss term that regularizes the self-attention map during pretraining, capturing the vertical convection process from input wind data. The new PWA SR-GAN shows the high-fidelity super-resolved 3D wind data, learns a wind structure at the high-frequency domain, and reduces the computational cost of a high-resolution wind simulation by x89.7 times.