3 papers
astro-ph.CO2021
Reconstruction of the Density Power Spectrum from Quasar Spectra using Machine Learning
Maria Han Veiga, Xi Meng, Oleg Y. Gnedin +2
We describe a novel end-to-end approach using Machine Learning to reconstruct the power spectrum of cosmological density perturbations at high redshift from observed quasar spectra…
math.PR2018
Entropy-based closure for probabilistic learning on manifolds
C. Soizea, R. Ghanem, C. Safta +7
In a recent paper, the authors proposed a general methodology for probabilistic learning on manifolds. The method was used to generate numerical samples that are statistically cons…
stat.ME2016
Sequential Bayesian optimal experimental design via approximate dynamic programming
Xun Huan, Youssef M. Marzouk
The design of multiple experiments is commonly undertaken via suboptimal strategies, such as batch (open-loop) design that omits feedback or greedy (myopic) design that does not ac…