
Highlyscalable, physicsinformed GANs for learning solutions of stochastic PDEs
Uncertainty quantification for forward and inverse problems is a central...
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Spherical CNNs on Unstructured Grids
We present an efficient convolution kernel for Convolutional Neural Netw...
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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...
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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...
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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...
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MeshfreeFlowNet: A PhysicsConstrained Deep Continuous SpaceTime SuperResolution Framework
We propose MeshfreeFlowNet, a novel deep learningbased superresolution...
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Graph Neural Networks for IceCube Signal Classification
Tasks involving the analysis of geometric (graph and manifoldstructure...
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Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale
Probabilistic programming languages (PPLs) are receiving widespread atte...
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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...
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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 ...
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ExtremeWeather: A largescale climate dataset for semisupervised detection, localization, and understanding of extreme weather events
Then detection and identification of extreme weather events in largesca...
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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...
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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...
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Scalable Bayesian Optimization Using Deep Neural Networks
Bayesian optimization is an effective methodology for the global optimiz...
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Improvements to Inference Compilation for Probabilistic Programming in LargeScale Scientific Simulators
We consider the problem of Bayesian inference in the family of probabili...
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Deep Neural Networks for Physics Analysis on lowlevel wholedetector data at the LHC
There has been considerable recent activity applying deep convolutional ...
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An Assessment of Data Transfer Performance for LargeScale Climate Data Analysis and Recommendations for the Data Infrastructure for CMIP6
We document the data transfer workflow, data transfer performance, and o...
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Scaling GRPC Tensorflow on 512 nodes of Cori Supercomputer
We explore scaling of the standard distributed Tensorflow with GRPC prim...
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Cataloging the Visible Universe through Bayesian Inference at Petascale
Astronomical catalogs derived from widefield imaging surveys are an imp...
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Approximate Inference for Constructing Astronomical Catalogs from Images
We present a new, fully generative model for constructing astronomical c...
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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...
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Deep Learning at 15PF: Supervised and SemiSupervised Classification for Scientific Data
This paper presents the first, 15PetaFLOP Deep Learning system for solv...
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Galactos: Computing the Anisotropic 3Point Correlation Function for 2 Billion Galaxies
The nature of dark energy and the complete theory of gravity are two cen...
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Exascale Deep Learning for Climate Analytics
We extract pixellevel masks of extreme weather patterns using variants ...
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Accelerating LargeScale Data Analysis by Offloading to HighPerformance Computing Libraries using Alchemist
Apache Spark is a popular system aimed at the analysis of large data set...
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Alchemist: An Apache Spark <=> MPI Interface
The Apache Spark framework for distributed computation is popular in the...
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Towards Unsupervised Segmentation of Extreme Weather Events
Extreme weather is one of the main mechanisms through which climate chan...
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DisCo: PhysicsBased Unsupervised Discovery of Coherent Structures in Spatiotemporal Systems
Extracting actionable insight from complex unlabeled scientific data is ...
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Track Seeding and Labelling with Embeddedspace Graph Neural Networks
To address the unprecedented scale of HLLHC data, the Exa.TrkX project ...
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Prabhat
verfied profile
Data and Analytics Group Lead at NERSC, Berkeley Lab