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Found 10 papers, 7 papers with code
Deploying deep learning in OpenFOAM with TensorFlow
We outline the development of a data science module within OpenFOAM which allows for the in-situ deployment of trained deep learning architectures for general-purpose predictive tasks.
Bayesian Neural Networks at Scale: A Performance Analysis and Pruning Study
This analysis of training a BNN at scale outlines the limitations and benefits compared to a conventional neural network.
Enabling real-time multi-messenger astrophysics discoveries with deep learning
Multi-messenger astrophysics is a fast-growing, interdisciplinary field that combines data, which vary in volume and speed of data processing, from many different instruments that probe the Universe using different cosmic messengers: electromagnetic waves, cosmic rays, gravitational waves and neutrinos.
Scaling Distributed Training of Flood-Filling Networks on HPC Infrastructure for Brain Mapping
Mapping all the neurons in the brain requires automatic reconstruction of entire cells from volume electron microscopy data.
Deep Learning for Multi-Messenger Astrophysics: A Gateway for Discovery in the Big Data Era
We discuss key aspects to realize this endeavor, namely (i) the design and exploitation of scalable and computationally efficient AI algorithms for Multi-Messenger Astrophysics; (ii) cyberinfrastructure requirements to numerically simulate astrophysical sources, and to process and interpret Multi-Messenger Astrophysics data; (iii) management of gravitational wave detections and triggers to enable electromagnetic and astro-particle follow-ups; (iv) a vision to harness future developments of machine and deep learning and cyberinfrastructure resources to cope with the scale of discovery in the Big Data Era; (v) and the need to build a community that brings domain experts together with data scientists on equal footing to maximize and accelerate discovery in the nascent field of Multi-Messenger Astrophysics.
Deep Learning at Scale for the Construction of Galaxy Catalogs in the Dark Energy Survey
Furthermore, we use our neural network model as a feature extractor for unsupervised clustering and find that unlabeled DES images can be grouped together in two distinct galaxy classes based on their morphology, which provides a heuristic check that the learning is successfully transferred to the classification of unlabelled DES images.
Umbrella sampling: a powerful method to sample tails of distributions
We present the umbrella sampling (US) technique and show that it can be used to sample extremely low probability areas of the posterior distribution that may be required in statistical analyses of data.
Instrumentation and Methods for Astrophysics
Biased Tracers in Redshift Space in the EFT of Large-Scale Structure
The Effective Field Theory of Large-Scale Structure (EFTofLSS) provides a novel formalism that is able to accurately predict the clustering of large-scale structure (LSS) in the mildly non-linear regime.
Cosmology and Nongalactic Astrophysics General Relativity and Quantum Cosmology High Energy Physics - Phenomenology High Energy Physics - Theory
astroABC: An Approximate Bayesian Computation Sequential Monte Carlo sampler for cosmological parameter estimation
Given the complexity of modern cosmological parameter inference where we are faced with non-Gaussian data and noise, correlated systematics and multi-probe correlated data sets, the Approximate Bayesian Computation (ABC) method is a promising alternative to traditional Markov Chain Monte Carlo approaches in the case where the Likelihood is intractable or unknown.
Instrumentation and Methods for Astrophysics Applications
Forward Modeling of Large-Scale Structure: An open-source approach with Halotools
We present the first stable release of Halotools (v0. 2), a community-driven Python package designed to build and test models of the galaxy-halo connection.
Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies
