Diffusion Model Based on Transformer
Yachen Tian, Ruien Zhang, Tianrui Li · 2024
The dandelion dispersal model holds significant importance in the fields of ecology and environmental science. However, traditional models are often constrained by complex environmental factors and plant characteristics. This paper proposes a novel dandelion dispersal model based on the Transformer architecture, aiming to enhance prediction accuracy and simulation effectiveness using deep learning techniques. We apply the Transformer model to the encoder-decoder structure of the dandelion dispersal model, leveraging self-attention mechanisms to capture intricate relationships between input sequences and achieve adaptive generation of output sequences. Experimental results demonstrate significant advantages of the Transformer-based dandelion dispersal model in simulating and predicting the spread of dandelion seeds, providing new insights and methods for research in ecology and environmental science.