GPLFR is a Gaussian process model that analytically marginalizes decoder weights to couple latent factor compression with prediction for high-dimensional low-data regression, demonstrated via the first spatially resolved emulator of rocky exoplanet global climate models.
and Kopparapu, Ravi kumar and Villanueva, Geronimo L
2 Pith papers cite this work. Polarity classification is still indexing.
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Climate states on exoplanets with the same atmospheric composition create different reflectance spectra, changing the detectability of atmospheric features and biosignatures, with seasonal variations on high-obliquity worlds adding time-dependent signals.
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Gaussian Process Latent Factor Regression for Low-Data, High-Dimensional Output Problems
GPLFR is a Gaussian process model that analytically marginalizes decoder weights to couple latent factor compression with prediction for high-dimensional low-data regression, demonstrated via the first spatially resolved emulator of rocky exoplanet global climate models.
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Impact of Climate States and Seasons on Future Exo-Earth Observations
Climate states on exoplanets with the same atmospheric composition create different reflectance spectra, changing the detectability of atmospheric features and biosignatures, with seasonal variations on high-obliquity worlds adding time-dependent signals.