Atmospheric Diffusion-Guided Spatio-Temporal Transformer for Nuclear Radiation Forecasting
Bridging atmospheric physics and deep learning for precise, nationwide radiation incident monitoring.
Nuclear radiation, the energy released during atomic decay, poses persistent risks to public health and the environment, and concerns have only grown since the Fukushima accident and the recent commencement of treated-water discharge. Modern monitoring networks now record radiati...
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This research bridges the gap between deep learning and atmospheric physics to solve the challenge of accurately tracking radiation dispersion in complex, non-stationary environments. By embedding physical dispersion laws directly into a transformer architecture, the model provides a robust tool for emergency response and long-term public health monitoring.
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