Prabhat

发表

Towards Unsupervised Segmentation of Extreme Weather Events pdf

Adam Rupe, Karthik Kashinath, Nalini Kumar, 2019, ArXiv.

Spherical CNNs on Unstructured Grids pdf

Matthias Nießner, Karthik Kashinath, Prabhat, 2019, ICLR.

Matrix Factorization at Scale: a Comparison of Scientific Data Analytics in Spark and C+MPI Using Three Case Studies pdf

James Demmel, Prabhat, Michael W. Mahoney, 2016, ArXiv.

Acceleration of Scientific Deep Learning Models on Heterogeneous Computing Platform with Intel® FPGAs

Prabhat, Chao Jiang, Thorsten Kurth, 2019, ISC Workshops.

Evaluation of HPC Application I/O on Object Storage Systems

Prabhat, Suren Byna, Quincey Koziol, 2018, 2018 IEEE/ACM 3rd International Workshop on Parallel Data Storage & Data Intensive Scalable Computing Systems (PDSW-DISCS).

Optimizing I/O Performance of HPC Applications with Autotuning

Surendra Byna, Prabhat, Marc Snir, 2019, ACM Trans. Parallel Comput..

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

Prabhat, Gilles Louppe, Frank D. Wood, 2017, ArXiv.

ArrayBridge: Interweaving Declarative Array Processing in SciDB with Imperative HDF5-Based Programs

Kesheng Wu, Paul Brown, Prabhat, 2018, 2018 IEEE 34th International Conference on Data Engineering (ICDE).

Taming parallel I/O complexity with auto-tuning

Surendra Byna, Prabhat, Marc Snir, 2013, 2013 SC - International Conference for High Performance Computing, Networking, Storage and Analysis (SC).

Accelerating analysis of void space in porous materials on multicore and GPU platforms

David Donofrio, Prabhat, Maciej Haranczyk, 2012, Int. J. High Perform. Comput. Appl..

Occam's razor and petascale visual data analysis

John B. Bell, Oliver Rübel, Prabhat, 2009 .

Highly-Ccalable, Physics-Informed GANs for Learning Solutions of Stochastic PDEs

Prabhat, Liu Yang, Thorsten Kurth, 2019, 2019 IEEE/ACM Third Workshop on Deep Learning on Supercomputers (DLS).

Performance analysis of emerging data analytics and HPC workloads

Prabhat, Christopher S. Daley, Nicholas J. Wright, 2017, PDSW-DISCS@SC.

Abstract: Auto-Tuning of Parallel IO Parameters for HDF5 Applications

Surendra Byna, Prabhat, Quincey Koziol, 2012, 2012 SC Companion: High Performance Computing, Networking Storage and Analysis.

Enforcing statistical constraints in generative adversarial networks for modeling chaotic dynamical systems

Karthik Kashinath, Prabhat, Heng Xiao, 2019, J. Comput. Phys..

Application of Deep Convolutional Neural Networks for Detecting Extreme Weather in Climate Datasets pdf

Prabhat, Michael F. Wehner, Yunjie Liu, 2016, ArXiv.

Parallel Kriging Analysis for Large Spatial Datasets

Prabhat, E. Wes Bethel, Wei Zhuo, 2011, 2011 IEEE 11th International Conference on Data Mining Workshops.

Accelerating Science with the NERSC Burst Buffer Early User Program

Surendra Byna, Prabhat, Gunther H. Weber, 2016 .

High-Performance Computing in Neuroscience for Data-Driven Discovery, Integration, and Dissemination

Kristofer E. Bouchard, David Donofrio, Prabhat, 2016, Neuron.

A Generalized Framework for Auto-tuning Stencil Computations

Samuel Williams, Leonid Oliker, John Shalf, 2009 .

Extreme Scaling of Production Visualization Software on Diverse Architectures

Prabhat, Gunther H. Weber, David Pugmire, 2010, IEEE Computer Graphics and Applications.

Techniques for modeling large-scale HPC I/O workloads

Robert Latham, Robert B. Ross, Surendra Byna, 2015, PMBS '15.

Cataloging the Visible Universe Through Bayesian Inference at Petascale

Prabhat, Steve Howard, Kiran Pamnany, 2018, 2018 IEEE International Parallel and Distributed Processing Symposium (IPDPS).

Alchemist: An Apache Spark ⇔ MPI interface

Prabhat, Michael W. Mahoney, Kristyn J. Maschhoff, 2018, Concurr. Comput. Pract. Exp..

BD-CATS: big data clustering at trillion particle scale

Surendra Byna, Prabhat, Pradeep Dubey, 2015, SC15: International Conference for High Performance Computing, Networking, Storage and Analysis.

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

Adam Rupe, Karthik Kashinath, Prabhat, 2019, 2019 IEEE/ACM Workshop on Machine Learning in High Performance Computing Environments (MLHPC).

Enforcing Physical Constraints in CNNs through Differentiable PDE Layer

Karthik Kashinath, Prabhat, Chiyu Max Jiang, 2020, ICLR 2020.

DOE's SciDAC Visualization and Analytics Center for EnablingTechnologies -- Strategy for Petascale Visual Data Analysis Success

Oliver Rübel, Prabhat, Valerio Pascucci, 2007 .

A Multi-Platform Evaluation of the Randomized CX Low-Rank Matrix Factorization in Spark

Oliver Rübel, Prabhat, Michael W. Mahoney, 2016, 2016 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW).

Experiences with 100Gbps network applications

John Shalf, Prabhat, Brian Tierney, 2012, DIDC '12.

International Neuroscience Initiatives through the Lens of High-Performance Computing

Oliver Rübel, Kristofer E. Bouchard, David Donofrio, 2018, Computer.

Testing the Reliability of Interpretable Neural Networks in Geoscience Using the Madden-Julian Oscillation

Karthik Kashinath, Prabhat, Da Yang, 2019 .

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

Prabhat, Pradeep Dubey, Brian Austin, 2017, SC17: International Conference for High Performance Computing, Networking, Storage and Analysis.

ClimateNet: an expert-labelled open dataset and Deep Learning architecture for enabling high-precision analyses of extreme weather

Sol Kim, Prabhat, Thorsten Kurth, 2020 .

Deep learning and process understanding for data-driven Earth system science

Joachim Denzler, Prabhat, Nuno Carvalhais, 2019, Nature.

Graph Neural Networks for Particle Reconstruction in High Energy Physics detectors

Prabhat, Gabriel Perdue, Paolo Calafiura, 2020, 2003.11603.

Scalable Bayesian Optimization Using Deep Neural Networks

Prabhat, Jasper Snoek, Ryan P. Adams, 2015, ICML.

Experiences in driving a cave with IBM scalable graphics engine-3 (SGE-3) prototypes

Prabhat, Samuel G. Fulcomer, 2005, VRST '05.

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

Prabhat, Eli Dart, Michael F. Wehner, 2017, ArXiv.

Graph Neural Networks for IceCube Signal Classification

Prabhat, Joan Bruna, Zahra Ronaghi, 2018, 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA).

Enforcing Physical Constraints in Neural Neural Networks through Differentiable PDE Layer

Karthik Kashinath, Prabhat, Chiyu Max Jiang, 2019 .

CosmoFlow: Using Deep Learning to Learn the Universe at Scale

Prabhat, Jason Sewall, Deborah Bard, 2018, SC18: International Conference for High Performance Computing, Networking, Storage and Analysis.

Developments in Architectures and Services for using High Performance Computing in Energy Frontier Experiments

Prabhat, John Taylor Childers, Vakhtang Tsulaia, 2016 .

Matrix factorizations at scale: A comparison of scientific data analytics in spark and C+MPI using three case studies

James Demmel, Pramod Sharma, Prabhat, 2016, 2016 IEEE International Conference on Big Data (Big Data).

High performance multivariate visual data exploration for extremely large data

Hans Hagen, Bernd Hamann, Kesheng Wu, 2008, 2008 SC - International Conference for High Performance Computing, Networking, Storage and Analysis.

Pattern-driven parallel I/O tuning

Surendra Byna, Prabhat, Marc Snir, 2015, PDSW '15.

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

Prabhat, Christopher Joseph Pal, Christopher Beckham, 2016, NIPS.

Tutorial on Machine Learning and Deep Learning for the Environmental and Geosciences

Karthik Kashinath, Prabhat, Anuj Karpatne, 2019 .

Searching for millions of objects in the BOSS spectroscopic survey data with H5Boss

Prabhat, Quincey Koziol, Jialin Liu, 2017, 2017 New York Scientific Data Summit (NYSDS).

Union of Intersections ( UoI ) for interpretable data driven discovery and prediction in neuroscience

Kristofer E. Bouchard, Prabhat, Michael W. Mahoney, 2018 .

A 3D Geoscience Data Visualization System for Mars Applied to Undergraduate Laboratories

Prabhat, Andrew S. Forsberg, James W. Head, 2007 .

Automated Analysis for Detecting Beams in Laser Wakefield Simulations

Oliver Rübel, Prabhat, Cecilia R. Aragon, 2008, 2008 Seventh International Conference on Machine Learning and Applications.

Cataloging the visible universe through Bayesian inference in Julia at petascale

Prabhat, Steve Howard, Kiran Pamnany, 2019, J. Parallel Distributed Comput..

Learning to Focus and Track Extreme Climate Events

Hyojin Kim, Prabhat, Sookyung Kim, 2019, BMVC.

Parallel index and query for large scale data analysis

Arie Shoshani, Kesheng Wu, Oliver Rübel, 2011, 2011 International Conference for High Performance Computing, Networking, Storage and Analysis (SC).

Scaling GRPC Tensorflow on 512 nodes of Cori Supercomputer pdf

Prabhat, Victor W. Lee, Thorsten Kurth, 2017, ArXiv.

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

Prabhat, Michael W. Mahoney, Kristyn J. Maschhoff, 2018, KDD.

TECA: Petascale Pattern Recognition for Climate Science

Surendra Byna, Prabhat, Eli Dart, 2015, CAIP.

Parallel I/O, analysis, and visualization of a trillion particle simulation

Arie Shoshani, Surendra Byna, Kesheng Wu, 2012, 2012 International Conference for High Performance Computing, Networking, Storage and Analysis.

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

Prabhat, Thorsten Kurth, Michela Paganini, 2017, Journal of Physics: Conference Series.

The HEP.TrkX Project: Deep Learning for Particle Tracking

Prabhat, Paolo Calafiura, Giuseppe Benedetto Cerati, 2018, Journal of Physics: Conference Series.

Deep Learning for Scientific Inference from Geophysical Data: The Madden-Julian Oscillation as a Test Case

Karthik Kashinath, Prabhat, Da Yang, 2019, 1902.04621.

Exascale Deep Learning for Climate Analytics

Prabhat, Massimiliano Fatica, Thorsten Kurth, 2018, SC18: International Conference for High Performance Computing, Networking, Storage and Analysis.

Optimizing the Union of Intersections LASSO (UoILASSO) and Vector Autoregressive (UoIVAR) Algorithms for Improved Statistical Estimation at Scale pdf

Aviral Shrivastava, Kristofer E. Bouchard, Prabhat, 2018, ArXiv.

The HEP.TrkX Project: deep neural networks for HL-LHC online and offline tracking

Prabhat, P. Spentzouris, Paolo Calafiura, 2017 .

High performance multivariate visual data exploration for extremely large data

Prabhat, Gunther H. Weber, Kesheng Wu, 2008, HiPC 2008.

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

Karthik Kashinath, Prabhat, Kamyar Azizzadenesheli, 2020, SC20: International Conference for High Performance Computing, Networking, Storage and Analysis.

A framework for auto-tuning HDF5 applications

Surendra Byna, Prabhat, Quincey Koziol, 2013, HPDC.

FastQuery: A Parallel Indexing System for Scientific Data

Kesheng Wu, Prabhat, Jerry Chi-Yuan Chou, 2011, 2011 IEEE International Conference on Cluster Computing.

A System for Query Based Analysis and Visualization

Oliver Rübel, Prabhat, Gunther H. Weber, 2012, EuroVA@EuroVis.

Virtual Reality-Based Interactive Scientific Visualization Environments

Prabhat, Joseph J. LaViola, David H. Laidlaw, 2009 .

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

Ioannis Mitliagkas, Prabhat, Pradeep Dubey, 2017, SC17: International Conference for High Performance Computing, Networking, Storage and Analysis.

A Gaussian Process Model of Quasar Spectral Energy Distributions

Prabhat, Albert Wu, Ryan P. Adams, 2015, NIPS.

High Performance Parallel I/O

Prabhat, Quincey Koziol, Q. Koziol, 2014 .

Methods for Specifying Scientific Data Standards and Modeling Relationships with Applications to Neuroscience

Oliver Rübel, Kristofer E. Bouchard, Prabhat, 2016, Front. Neuroinform..

Virtual Field Trip to Mars: Experiences with a Virtual Reality Lab for Undergraduate Students

Prabhat, Andrew S. Forsberg, Noah E. Petro, 2006 .

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

Prabhat, Pierre Baldi, Sang-Yun Oh, 2016, ICMLA 2016.

Artificial Neural Network

Prabhat, B. Yegnanarayana, Virag Jagtap, 2018, Encyclopedia of GIS.

Deep-Hurricane-Tracker: Tracking and Forecasting Extreme Climate Events

Hyojin Kim, Karthik Kashinath, Prabhat, 2019, 2019 IEEE Winter Conference on Applications of Computer Vision (WACV).

Why high performance visual data analytics is both relevant and difficult

Surendra Byna, Oliver Rübel, Prabhat, 2013, Electronic Imaging.

Etalumis: bringing probabilistic programming to scientific simulators at scale

Prabhat, Gilles Louppe, Frank D. Wood, 2019, SC.

PANDA: Extreme Scale Parallel K-Nearest Neighbor on Distributed Architectures

Surendra Byna, Prabhat, Pradeep Dubey, 2016, 2016 IEEE International Parallel and Distributed Processing Symposium (IPDPS).

BRAINformat: A Data Standardization Framework for Neuroscience Data

Kristofer E. Bouchard, Prabhat, Oliver Ruebel, 2015, bioRxiv.

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

Prabhat, Kiran Pamnany, David Schlegel, 2016, ArXiv.

Coupling visualization and data analysis for knowledge discovery from multi-dimensional scientific data

Hans Hagen, Bernd Hamann, Oliver Rübel, 2010, ICCS.

Neural Network Optimization Under Partial Differential Equation Constraints

Karthik Kashinath, Prabhat, Philip S. Marcus, 2019 .

A Multiplatform Study of I/O Behavior on Petascale Supercomputers

Marianne Winslett, Robert B. Ross, Surendra Byna, 2015, HPDC.

Adviser: immersive field work for planetary geoscientists

Prabhat, David Shean, Mark A. Duchaineau, 2006, IEEE Computer Graphics and Applications.

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

Prabhat, Gilles Louppe, Frank D. Wood, 2018, NeurIPS.

Machine Learning and Understanding for Intelligent Extreme Scale Scientific Computing and Discovery. DOE Workshop Report, January 7–9, 2015, Rockville, MD

Prasanna Balaprakash, Prabhat, Thomas E. Potok, 2015 .

Parallelizing Gaussian Process Calculations in R pdf

Prabhat, Benjamin Lipshitz, Wei Zhuo, 2013, ArXiv.

Bridging simulation and deep learning - convolutional neural networks on unstructured grids

Karthik Kashinath, Prabhat, Chiyu Jiang, 2018 .

Semi-Supervised Detection of Extreme Weather Events in Large Climate Datasets pdf

Prabhat, Christopher Joseph Pal, Christopher Beckham, 2016, ArXiv.

Dynamic Model-Driven Parallel I/O Performance Tuning

Surendra Byna, Prabhat, Stefan M. Wild, 2015, 2015 IEEE International Conference on Cluster Computing.

ADVISER: Immersive Scientific Visualization Applied to Mars Research and Exploration

Prabhat, Andrew S. Forsberg, Andries van Dam, 2005 .

Ultrascale Visualization of Climate Data

Prabhat, Dean N. Williams, Aashish Chaudhary, 2013, Computer.

Approximate Inference for Constructing Astronomical Catalogs from Images pdf

Prabhat, Ryan P. Adams, David Schlegel, 2018, The Annals of Applied Statistics.

Improving parallel I/O autotuning with performance modeling

Surendra Byna, Prabhat, Stefan M. Wild, 2014, HPDC '14.

Data Elevator: Low-Contention Data Movement in Hierarchical Storage System

Kesheng Wu, Bin Dong, Prabhat, 2016, 2016 IEEE 23rd International Conference on High Performance Computing (HiPC).

Visualization Platforms for Scientific Exploration of Mars

Prabhat, Andrew S. Forsberg, James W. Head, 2007 .

Interactive Distributed Deep Learning with Jupyter Notebooks

Prabhat, Matthew Henderson, Shreyas Cholia, 2018, ISC Workshops.

Inferring Attentional State and Kinematics from Motor Cortical Firing Rates

Prabhat, F. Wood, J.P. Donoghue, 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

TECA: A Parallel Toolkit for Extreme Climate Analysis

Surendra Byna, Kesheng Wu, Oliver Rübel, 2012, ICCS.

Topological data analysis and machine learning for recognizing atmospheric river patterns in large climate datasets

Prabhat, Vitaliy Kurlin, Grzegorz Muszynski, 2019, Geoscientific Model Development.

Exascale Deep Learning for Climate Science

Karthik Kashinath, Prabhat, Massimiliano Fatica, 2019 .

FastQuery: A General Indexing and Querying System for Scientific Data

Kesheng Wu, Prabhat, Jerry Chi-Yuan Chou, 2011, SSDBM.

H5hut: A high-performance I/O library for particle-based simulations

Prabhat, Mark Howison, Andreas Adelmann, 2010, 2010 IEEE International Conference On Cluster Computing Workshops and Posters (CLUSTER WORKSHOPS).

A Comparative Study of Desktop, Fishtank, and Cave Systems for the Exploration of Volume Rendered Confocal Data Sets

Prabhat, Mel Slater, Andrew S. Forsberg, 2008, IEEE Transactions on Visualization and Computer Graphics.

Detection of atmospheric rivers with inline uncertainty quantification: TECA-BARD v1.0.1

Prabhat, Harinarayan Krishnan, Travis A. O'Brien, 2020, Geoscientific Model Development.

ArrayBridge: Interweaving declarative array processing with high-performance computing pdf

Kesheng Wu, Paul Brown, Prabhat, 2017, ArXiv.

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

Prabhat, Pierre Baldi, Sang-Yun Oh, 2016, 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA).

A Parallel EM Algorithm for Model-Based Clustering Applied to the Exploration of Large Spatio-Temporal Data

Prabhat, Michael F. Wehner, David Pugmire, 2013, Technometrics.

Understanding the I/O Performance Gap Between Cori KNL and Haswell

Prabhat, Suren Byna, Brian Austin, 2017 .

Celeste: Variational inference for a generative model of astronomical images

Prabhat, Ryan P. Adams, Matthew D. Hoffman, 2015, ICML.

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

Peter J. Bickel, Kristofer E. Bouchard, Prabhat, 2021, ArXiv.

Atmospheric Blocking Pattern Recognition in Global Climate Model Simulation Data

Karthik Kashinath, Prabhat, Michael F. Wehner, 2021, 2020 25th International Conference on Pattern Recognition (ICPR).

A Physics-Based Approach to Unsupervised Discovery of Coherent Structures in Spatiotemporal Systems

Prabhat, J. Crutchfield, A. Rupe, 2017, 1709.03184.

Celeste : Scalable variational inference for a generative model of astronomical images

Prabhat, Ryan P. Adams, Jon D. McAuliffe, 2014 .

Title High performance multivariate visual data exploration for extremely large data Permalink

Prabhat, E. W. Bethel, B. Hamann, 2008 .

FastBit: interactively searching massive data

Prabhat, E. W. Bethel, B. Hamann, 2009 .

Identifying important ions and positions in mass spectrometry imaging data using CUR matrix decompositions.

Prabhat, Michael W. Mahoney, O. Rübel, 2015, Analytical chemistry.

Resolution Dependence of Future Tropical Cyclone Projections of CAM5.1 in the U.S. CLIVAR Hurricane Working Group Idealized Configurations

Prabhat, W. Collins, D. Stone, 2015 .

North American extreme temperature events and related large scale meteorological patterns: a review of statistical methods, dynamics, modeling, and trends

Prabhat, R. Leung, R. Katz, 2016, Climate Dynamics.

Physics-informed machine learning: case studies for weather and climate modelling

Prabhat, H. A. Tchelepi, R. Wang, 2021, Philosophical Transactions of the Royal Society A.

H 5 Spark : Bridging the I / O Gap between Spark and Scientific Data Formats on HPC Systems

Prabhat, Michael F. Ringenburg, Alex Gittens, 2016 .

Storage 2020: A Vision for the Future of HPC Storage

Prabhat, Nicholas J. Wright, Glenn K. Lockwood, 2017 .

Comparative Evaluation of Desktop and Cave Environments for Learning Hypercube Rotations

Prabhat, Cullen D. Jackson, T. Banchoff, 2022 .

Automated detection and analysis of particle beams in laser-plasma accelerator simulations

Prabhat, E. W. Bethel, B. Hamann, 2010 .

Novel deep learning methods for track reconstruction

Prabhat, M. Spiropulu, Stephan Zheng, 2018, 1810.06111.

Stereophotogrammetry of Oceanic Clouds

Prabhat, Paquita Zuidema, Rusen Oktem, 2014 .

Inferring the anthropogenic contribution to local temperature extremes

Prabhat, Michael Wehner, Christopher J Paciorek, 2013, Proceedings of the National Academy of Sciences.

The Union of Intersections (UoI) method for interpretable data driven discovery and prediction

Kristofer E. Bouchard, Prabhat, Sharmodeep Bhattacharyya, 2017, NIPS 2017.

Detecting atmospheric rivers in large climate datasets

Surendra Byna, Prabhat, Michael F. Wehner, 2011, PDAC '11.

Analysis of Kupffer's vesicle in zebrafish embryos using a cave automated virtual environment

Prabhat, Geoffrey Williams, J. Kreiling, 2007, Developmental dynamics : an official publication of the American Association of Anatomists.

A deep generative model for astronomical images of galaxies

Prabhat, Jeffrey Regier, Jon McAuliffe, 2015 .

The effect of horizontal resolution on simulation quality in the Community Atmospheric Model, CAM5.1

Prabhat, Michael F. Wehner, Kevin A. Reed, 2014 .

Computational Approaches for the High-Throughput Analysis of Porous Materials for Energy-related Applications

Prabhat, E. W. Bethel, J. Sethian, 2011 .

Simulation of Lennard-Jones Potential on a Quantum Computer

Prabhat, B. K. Behera, 2021, 2101.10202.

Track Seeding and Labelling with Embedded-space Graph Neural Networks pdf

Prabhat, Paolo Calafiura, Panagiotis Spentzouris, 2020, ArXiv.

Global North Pacific landfalling North Atlantic landfalling Southeastern U . S . South America Polar Parameter type Parameter choices

Prabhat, Anna M. Wilson, L. Leung, 2018 .

Deep learning with raw data from Daya Bay

Prabhat, W. Bhimji, S. Kohn, 2017 .

Characterization of extreme precipitation within atmospheric river events over California

Prabhat, W. Collins, S. Byna, 2015 .

Classification of nodal pockets in many-electron wave functions via machine learning

Prabhat, W. Lester, D. Zubarev, 2012, Journal of Mathematical Chemistry.

Magnetism and bonding in graphene nanodots with H modified interior, edge, and apex.

Prabhat, Y. Kawazoe, M. R. Philpott, 2011, The Journal of chemical physics.

Etalumis

Prabhat, Philip H. S. Torr, K. Cranmer, 2019, Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis.

Deep learning and process understanding for data-driven Earth system science

Prabhat, B. Stevens, Joachim Denzler, 2019, Nature.

Track Seeding and Labelling with Embedded-space Graph Neural Networks

Prabhat, M. Spiropulu, A. Aurisano, 2020, ArXiv.

Celeste: Variational inference for a generative model of astronomical images

Prabhat, Ryan P. Adams, Andrew C. Miller, 2015, ICML.

Atmospheric River Tracking Method Intercomparison Project (ARTMIP): project goals and experimental design

Daniel Walton, Karthik Kashinath, Prabhat, 2018, Geoscientific Model Development.

DOE High Performance Computing Operational Review (HPCOR): Enabling Data-Driven Scientific Discovery at HPC Facilities

Prabhat, Eli Dart, Laura Monroe, 2014 .

Particle Track Reconstruction with Deep Learning

Prabhat, S. Farrell, M. Mudigonda, 2017 .

Comparative Analysis of Deep Convolutional Generative Adversarial Network and Conditional Generative Adversarial Network using Hand Written Digits

Prabhat, Nishant, Prabhat, 2020, 2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS).

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

Prabhat, Michael W. Mahoney, Alejandro F. Bujan, 2017, NIPS.

Scaling of Union of Intersections for Inference of Granger Causal Networks from Observational Data

Kristofer E. Bouchard, Prabhat, Aviral Shrivastava, 2020, 2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS).

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

Prabhat, Md. Mostofa Ali Patwary, P. Dubey, 2017, SC17: International Conference for High Performance Computing, Networking, Storage and Analysis.

DOE's SciDAC Visualization and Analytics Center for EnablingTechnologies -- Strategy for Petascale Visual Data Analysis Success

Prabhat, Cláudio T. Silva, Christopher R. Johnson, 2007 .

Discovering Coherent Structures Using Local Causal States

Adam Rupe, Karthik Kashinath, Prabhat, 2017 .

TECA: Petascale Pattern Recognition for Climate Science

Prabhat, W. Collins, S. Byna, 2015, CAIP.

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

Prabhat, Mustafa A. Mustafa, K. Azizzadenesheli, 2020, SC20: International Conference for High Performance Computing, Networking, Storage and Analysis.