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Found 5 papers, 3 papers with code
Highly-scalable, physics-informed GANs for learning solutions of stochastic PDEs
Uncertainty quantification for forward and inverse problems is a central challenge across physical and biomedical disciplines.
A Differentiable Programming System to Bridge Machine Learning and Scientific Computing
Scientific computing is increasingly incorporating the advancements in machine learning and the ability to work with large amounts of data.
Fashionable Modelling with Flux
Machine learning as a discipline has seen an incredible surge of interest in recent years due in large part to a perfect storm of new theory, superior tooling, renewed interest in its capabilities.
Automatic Full Compilation of Julia Programs and ML Models to Cloud TPUs
Google's Cloud TPUs are a promising new hardware architecture for machine learning workloads.
Cataloging the Visible Universe through Bayesian Inference at Petascale
We construct an astronomical catalog from 55 TB of imaging data using Celeste, a Bayesian variational inference code written entirely in the high-productivity programming language Julia.
Distributed, Parallel, and Cluster Computing Instrumentation and Methods for Astrophysics 85A35, 68W10, 62P35 J.2; D.1.3; G.3; I.2; D.2
