Prabhat

Track Seeding and Labelling with Embedded-space Graph Neural Networks

To address the unprecedented scale of HL-LHC data, the Exa.TrkX project ...
06/30/2020 ∙ by Nicholas Choma, et al. ∙ 0 ∙ share

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MeshfreeFlowNet: A Physics-Constrained Deep Continuous Space-Time Super-Resolution Framework

We propose MeshfreeFlowNet, a novel deep learning-based super-resolution...
05/01/2020 ∙ by Chiyu "Max" Jiang, et al. ∙ 3 ∙ share

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Highly-scalable, physics-informed GANs for learning solutions of stochastic PDEs

Uncertainty quantification for forward and inverse problems is a central...
10/29/2019 ∙ by Liu Yang, et al. ∙ 42 ∙ share

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DisCo: Physics-Based Unsupervised Discovery of Coherent Structures in Spatiotemporal Systems

Extracting actionable insight from complex unlabeled scientific data is ...
09/25/2019 ∙ by Adam Rupe, et al. ∙ 0 ∙ share

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Towards Unsupervised Segmentation of Extreme Weather Events

Extreme weather is one of the main mechanisms through which climate chan...
09/16/2019 ∙ by Adam Rupe, et al. ∙ 0 ∙ share

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Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale

Probabilistic programming languages (PPLs) are receiving widespread atte...
07/08/2019 ∙ by Atılım Güneş Baydin, et al. ∙ 2 ∙ share

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Enforcing Statistical Constraints in Generative Adversarial Networks for Modeling Chaotic Dynamical Systems

Simulating complex physical systems often involves solving partial diffe...
05/13/2019 ∙ by Jin-Long Wu, et al. ∙ 6 ∙ share

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Spherical CNNs on Unstructured Grids

We present an efficient convolution kernel for Convolutional Neural Netw...
01/07/2019 ∙ by Chiyu "Max" Jiang, et al. ∙ 14 ∙ share

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Exascale Deep Learning for Climate Analytics

We extract pixel-level masks of extreme weather patterns using variants ...
10/03/2018 ∙ by Thorsten Kurth, et al. ∙ 0 ∙ share

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Graph Neural Networks for IceCube Signal Classification

Tasks involving the analysis of geometric (graph- and manifold-structure...
09/17/2018 ∙ by Nicholas Choma, et al. ∙ 2 ∙ share

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Optimizing the Union of Intersections LASSO (UoI_LASSO) and Vector Autoregressive (UoI_VAR) Algorithms for Improved Statistical Estimation at Scale

The analysis of scientific data of increasing size and complexity requir...
08/21/2018 ∙ by Mahesh Balasubramanian, et al. ∙ 8 ∙ share

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CosmoFlow: Using Deep Learning to Learn the Universe at Scale

Deep learning is a promising tool to determine the physical model that d...
08/14/2018 ∙ by Amrita Mathuriya, et al. ∙ 0 ∙ share

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Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model

We present a novel framework that enables efficient probabilistic infere...
07/20/2018 ∙ by Atilim Gunes Baydin, et al. ∙ 4 ∙ share

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Alchemist: An Apache Spark <=> MPI Interface

The Apache Spark framework for distributed computation is popular in the...
06/03/2018 ∙ by Alex Gittens, et al. ∙ 0 ∙ share

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Accelerating Large-Scale Data Analysis by Offloading to High-Performance Computing Libraries using Alchemist

Apache Spark is a popular system aimed at the analysis of large data set...
05/30/2018 ∙ by Alex Gittens, et al. ∙ 0 ∙ share

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Approximate Inference for Constructing Astronomical Catalogs from Images

We present a new, fully generative model for constructing astronomical c...
02/28/2018 ∙ by Jeffrey Regier, et al. ∙ 0 ∙ share

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Cataloging the Visible Universe through Bayesian Inference at Petascale

Astronomical catalogs derived from wide-field imaging surveys are an imp...
01/31/2018 ∙ by Jeffrey Regier, et al. ∙ 0 ∙ share

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Scaling GRPC Tensorflow on 512 nodes of Cori Supercomputer

We explore scaling of the standard distributed Tensorflow with GRPC prim...
12/26/2017 ∙ by Amrita Mathuriya, et al. ∙ 0 ∙ share

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Improvements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators

We consider the problem of Bayesian inference in the family of probabili...
12/21/2017 ∙ by Mario Lezcano Casado, et al. ∙ 0 ∙ share

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Deep Neural Networks for Physics Analysis on low-level whole-detector data at the LHC

There has been considerable recent activity applying deep convolutional ...
11/09/2017 ∙ by Wahid Bhimji, et al. ∙ 0 ∙ share

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Galactos: Computing the Anisotropic 3-Point Correlation Function for 2 Billion Galaxies

The nature of dark energy and the complete theory of gravity are two cen...
08/31/2017 ∙ by Brian Friesen, et al. ∙ 0 ∙ share

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An Assessment of Data Transfer Performance for Large-Scale Climate Data Analysis and Recommendations for the Data Infrastructure for CMIP6

We document the data transfer workflow, data transfer performance, and o...
08/26/2017 ∙ by Eli Dart, et al. ∙ 0 ∙ share

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Deep Learning at 15PF: Supervised and Semi-Supervised Classification for Scientific Data

This paper presents the first, 15-PetaFLOP Deep Learning system for solv...
08/17/2017 ∙ by Thorsten Kurth, et al. ∙ 0 ∙ share

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Union of Intersections (UoI) for Interpretable Data Driven Discovery and Prediction

The increasing size and complexity of scientific data could dramatically...
05/22/2017 ∙ by Kristofer E. Bouchard, et al. ∙ 0 ∙ share

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ExtremeWeather: A large-scale climate dataset for semi-supervised detection, localization, and understanding of extreme weather events

Then detection and identification of extreme weather events in large-sca...
12/07/2016 ∙ by Evan Racah, et al. ∙ 0 ∙ share

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Learning an Astronomical Catalog of the Visible Universe through Scalable Bayesian Inference

Celeste is a procedure for inferring astronomical catalogs that attains ...
11/10/2016 ∙ by Jeffrey Regier, et al. ∙ 0 ∙ share

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Revealing Fundamental Physics from the Daya Bay Neutrino Experiment using Deep Neural Networks

Experiments in particle physics produce enormous quantities of data that...
01/28/2016 ∙ by Evan Racah, et al. ∙ 0 ∙ share

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Celeste: Variational inference for a generative model of astronomical images

We present a new, fully generative model of optical telescope image sets...
06/03/2015 ∙ by Jeffrey Regier, et al. ∙ 0 ∙ share

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Scalable Bayesian Optimization Using Deep Neural Networks

Bayesian optimization is an effective methodology for the global optimiz...
02/19/2015 ∙ by Jasper Snoek, et al. ∙ 0 ∙ share

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