Prototype-enhanced prediction in graph neural networks for climate applications

• URL: http://arxiv.org/abs/2504.17492v1
• Date: Thu, 24 Apr 2025 12:34:23 GMT
• Title: Prototype-enhanced prediction in graph neural networks for climate applications
• Authors: Nawid Keshtmand, Elena Fillola, Jeffrey Nicholas Clark, Raul Santos-Rodriguez, Matthew Rigby,
• Abstract summary: Data-driven emulators are increasingly being used to learn and emulate physics-based simulations.<n>We present a structured way to improve the quality of these high-dimensional emulated outputs, through the use of prototypes.<n>We demonstrate our approach to emulate atmospheric dispersion, key for greenhouse gas emissions monitoring, by comparing a baseline model to models trained using prototypes.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Data-driven emulators are increasingly being used to learn and emulate physics-based simulations, reducing computational expense and run time. Here, we present a structured way to improve the quality of these high-dimensional emulated outputs, through the use of prototypes: an approximation of the emulator's output passed as an input, which informs the model and leads to better predictions. We demonstrate our approach to emulate atmospheric dispersion, key for greenhouse gas emissions monitoring, by comparing a baseline model to models trained using prototypes as an additional input. The prototype models achieve better performance, even with few prototypes and even if they are chosen at random, but we show that choosing the prototypes through data-driven methods (k-means) can lead to almost 10\% increased performance in some metrics.

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