Data-driven emulators are increasingly being used to learn and emulate
physics-based simulations, reducing computational expense and run time. Ici,
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.

Cet article explore les excursions dans le temps et leurs implications.

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