A 3D super-resolution of wind fields via physics-informed pixel-wise self-attention generative adversarial network

• URL: http://arxiv.org/abs/2312.13212v1
• Date: Wed, 20 Dec 2023 17:28:21 GMT
• Title: A 3D super-resolution of wind fields via physics-informed pixel-wise self-attention generative adversarial network
• Authors: Takuya Kurihana, Kyongmin Yeo, Daniela Szwarcman, Bruce Elmegreen, Karthik Mukkavilli, Johannes Schmude, Levente Klein
• Abstract summary: complexity of computation in resolving high-resolution wind fields left 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) GAN super-resolves the three-dimensional (3D) low-resolution wind fields by upscaling x9 times.
• Score: 0.01649298969786889
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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