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Found 4 papers, 2 papers with code
Parallelized Acquisition for Active Learning using Monte Carlo Sampling
Bayesian inference remains one of the most important tool-kits for any scientist, but increasingly expensive likelihood functions are required for ever-more complex experiments, raising the cost of generating a Monte Carlo sample of the posterior.
Fast and robust Bayesian Inference using Gaussian Processes with GPry
GPry does not need any pre-training, special hardware such as GPUs, and is intended as a drop-in replacement for traditional Monte Carlo methods for Bayesian inference.
Cosmological constraints on multi-interacting dark matter
The increasingly significant tensions within $\Lambda$CDM, combined with the lack of detection of dark matter (DM) in laboratory experiments, have boosted interest in non-minimal dark sectors, which are theoretically well-motivated and inspire new search strategies for DM.
Cosmology and Nongalactic Astrophysics High Energy Physics - Phenomenology
CosmicNet I: Physics-driven implementation of neural networks within Boltzmann-Einstein solvers
Einstein-Boltzmann Solvers (EBSs) are run on a massive scale by the cosmology community when fitting cosmological models to data.
Cosmology and Nongalactic Astrophysics
