
Track Seeding and Labelling with Embeddedspace Graph Neural Networks
To address the unprecedented scale of HLLHC data, the Exa.TrkX project ...
read it

MeshfreeFlowNet: A PhysicsConstrained Deep Continuous SpaceTime SuperResolution Framework
We propose MeshfreeFlowNet, a novel deep learningbased superresolution...
read it

Highlyscalable, physicsinformed GANs for learning solutions of stochastic PDEs
Uncertainty quantification for forward and inverse problems is a central...
read it

DisCo: PhysicsBased Unsupervised Discovery of Coherent Structures in Spatiotemporal Systems
Extracting actionable insight from complex unlabeled scientific data is ...
read it

Towards Unsupervised Segmentation of Extreme Weather Events
Extreme weather is one of the main mechanisms through which climate chan...
read it

Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale
Probabilistic programming languages (PPLs) are receiving widespread atte...
read it

Enforcing Statistical Constraints in Generative Adversarial Networks for Modeling Chaotic Dynamical Systems
Simulating complex physical systems often involves solving partial diffe...
read it

Spherical CNNs on Unstructured Grids
We present an efficient convolution kernel for Convolutional Neural Netw...
read it

Exascale Deep Learning for Climate Analytics
We extract pixellevel masks of extreme weather patterns using variants ...
read it

Graph Neural Networks for IceCube Signal Classification
Tasks involving the analysis of geometric (graph and manifoldstructure...
read it

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...
read it

CosmoFlow: Using Deep Learning to Learn the Universe at Scale
Deep learning is a promising tool to determine the physical model that d...
read it

Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model
We present a novel framework that enables efficient probabilistic infere...
read it

Alchemist: An Apache Spark <=> MPI Interface
The Apache Spark framework for distributed computation is popular in the...
read it

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...
read it

Approximate Inference for Constructing Astronomical Catalogs from Images
We present a new, fully generative model for constructing astronomical c...
read it

Cataloging the Visible Universe through Bayesian Inference at Petascale
Astronomical catalogs derived from widefield imaging surveys are an imp...
read it

Scaling GRPC Tensorflow on 512 nodes of Cori Supercomputer
We explore scaling of the standard distributed Tensorflow with GRPC prim...
read it

Improvements to Inference Compilation for Probabilistic Programming in LargeScale Scientific Simulators
We consider the problem of Bayesian inference in the family of probabili...
read it

Deep Neural Networks for Physics Analysis on lowlevel wholedetector data at the LHC
There has been considerable recent activity applying deep convolutional ...
read it

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...
read it

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...
read it

Deep Learning at 15PF: Supervised and SemiSupervised Classification for Scientific Data
This paper presents the first, 15PetaFLOP Deep Learning system for solv...
read it

Union of Intersections (UoI) for Interpretable Data Driven Discovery and Prediction
The increasing size and complexity of scientific data could dramatically...
read it

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...
read it

Learning an Astronomical Catalog of the Visible Universe through Scalable Bayesian Inference
Celeste is a procedure for inferring astronomical catalogs that attains ...
read it

Revealing Fundamental Physics from the Daya Bay Neutrino Experiment using Deep Neural Networks
Experiments in particle physics produce enormous quantities of data that...
read it

Celeste: Variational inference for a generative model of astronomical images
We present a new, fully generative model of optical telescope image sets...
read it

Scalable Bayesian Optimization Using Deep Neural Networks
Bayesian optimization is an effective methodology for the global optimiz...
read it
Prabhat
verfied profile
Data and Analytics Group Lead at NERSC, Berkeley Lab