b'Prabhat'

research

∙ 11/27/2021

Learning from learning machines: a new generation of AI technology to meet the needs of science

We outline emerging opportunities and challenges to enhance the utility ...

Luca Pion-Tonachini, et al. ∙

research

∙ 06/30/2020

Track Seeding and Labelling with Embedded-space Graph Neural Networks

To address the unprecedented scale of HL-LHC data, the Exa.TrkX project ...

Nicholas Choma, et al. ∙

research

∙ 05/01/2020

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

We propose MeshfreeFlowNet, a novel deep learning-based super-resolution...

Chiyu "Max" Jiang, et al. ∙

research

∙ 10/29/2019

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

Uncertainty quantification for forward and inverse problems is a central...

Liu Yang, et al. ∙

research

∙ 09/25/2019

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

Extracting actionable insight from complex unlabeled scientific data is ...

Adam Rupe, et al. ∙

research

∙ 09/16/2019

Towards Unsupervised Segmentation of Extreme Weather Events

Extreme weather is one of the main mechanisms through which climate chan...

Adam Rupe, et al. ∙

research

∙ 07/08/2019

Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale

Probabilistic programming languages (PPLs) are receiving widespread atte...

Atılım Güneş Baydin, et al. ∙

research

∙ 05/13/2019

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

Simulating complex physical systems often involves solving partial diffe...

Jin-Long Wu, et al. ∙

research

∙ 01/07/2019

Spherical CNNs on Unstructured Grids

We present an efficient convolution kernel for Convolutional Neural Netw...

Chiyu "Max" Jiang, et al. ∙

research

∙ 10/03/2018

Exascale Deep Learning for Climate Analytics

We extract pixel-level masks of extreme weather patterns using variants ...

Thorsten Kurth, et al. ∙

research

∙ 09/17/2018

Graph Neural Networks for IceCube Signal Classification

Tasks involving the analysis of geometric (graph- and manifold-structure...

Nicholas Choma, et al. ∙

research

∙ 08/21/2018

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...

Mahesh Balasubramanian, et al. ∙

research

∙ 08/14/2018

CosmoFlow: Using Deep Learning to Learn the Universe at Scale

Deep learning is a promising tool to determine the physical model that d...

Amrita Mathuriya, et al. ∙

research

∙ 07/20/2018

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

We present a novel framework that enables efficient probabilistic infere...

Atilim Gunes Baydin, et al. ∙

research

∙ 06/03/2018

Alchemist: An Apache Spark <=> MPI Interface

The Apache Spark framework for distributed computation is popular in the...

Alex Gittens, et al. ∙

research

∙ 05/30/2018

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...

Alex Gittens, et al. ∙

research

∙ 02/28/2018

Approximate Inference for Constructing Astronomical Catalogs from Images

We present a new, fully generative model for constructing astronomical c...

Jeffrey Regier, et al. ∙

research

∙ 01/31/2018

Cataloging the Visible Universe through Bayesian Inference at Petascale

Astronomical catalogs derived from wide-field imaging surveys are an imp...

Jeffrey Regier, et al. ∙

research

∙ 12/26/2017

Scaling GRPC Tensorflow on 512 nodes of Cori Supercomputer

We explore scaling of the standard distributed Tensorflow with GRPC prim...

Amrita Mathuriya, et al. ∙

research

∙ 12/21/2017

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

We consider the problem of Bayesian inference in the family of probabili...

Mario Lezcano Casado, et al. ∙

research

∙ 11/09/2017

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

There has been considerable recent activity applying deep convolutional ...

Wahid Bhimji, et al. ∙

research

∙ 08/31/2017

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...

Brian Friesen, et al. ∙

research

∙ 08/26/2017

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...

Eli Dart, et al. ∙

research

∙ 08/17/2017

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

This paper presents the first, 15-PetaFLOP Deep Learning system for solv...

Thorsten Kurth, et al. ∙

research

∙ 05/22/2017

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

The increasing size and complexity of scientific data could dramatically...

Kristofer E. Bouchard, et al. ∙

research

∙ 12/07/2016

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...

Evan Racah, et al. ∙

research

∙ 11/10/2016

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

Celeste is a procedure for inferring astronomical catalogs that attains ...

Jeffrey Regier, et al. ∙

research

∙ 01/28/2016

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

Experiments in particle physics produce enormous quantities of data that...

Evan Racah, et al. ∙

research

∙ 06/03/2015

Celeste: Variational inference for a generative model of astronomical images

We present a new, fully generative model of optical telescope image sets...

Jeffrey Regier, et al. ∙

research

∙ 02/19/2015

Scalable Bayesian Optimization Using Deep Neural Networks

Bayesian optimization is an effective methodology for the global optimiz...

Jasper Snoek, et al. ∙

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