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Found 2 papers, 2 papers with code
Active Learning for Deep Gaussian Process Surrogates
Deep Gaussian processes (DGPs) are increasingly popular as predictive models in machine learning (ML) for their non-stationary flexibility and ability to cope with abrupt regime changes in training data.
The Mira-Titan Universe: Precision Predictions for Dark Energy Surveys
The new sampling method allows us to build precision emulators from just 26 cosmological models and to increase the emulator accuracy by adding new sets of simulations in a prescribed way.
Cosmology and Nongalactic Astrophysics
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