no code implementations • 27 Nov 2021 • Luca Pion-Tonachini, Kristofer Bouchard, Hector Garcia Martin, Sean Peisert, W. Bradley Holtz, Anil Aswani, Dipankar Dwivedi, Haruko Wainwright, Ghanshyam Pilania, Benjamin Nachman, Babetta L. Marrone, Nicola Falco, Prabhat, Daniel Arnold, Alejandro Wolf-Yadlin, Sarah Powers, Sharlee Climer, Quinn Jackson, Ty Carlson, Michael Sohn, Petrus Zwart, Neeraj Kumar, Amy Justice, Claire Tomlin, Daniel Jacobson, Gos Micklem, Georgios V. Gkoutos, Peter J. Bickel, Jean-Baptiste Cazier, Juliane Müller, Bobbie-Jo Webb-Robertson, Rick Stevens, Mark Anderson, Ken Kreutz-Delgado, Michael W. Mahoney, James B. Brown

We outline emerging opportunities and challenges to enhance the utility of AI for scientific discovery.

no code implementations • 30 Jun 2020 • Nicholas Choma, Daniel Murnane, Xiangyang Ju, Paolo Calafiura, Sean Conlon, Steven Farrell, Prabhat, Giuseppe Cerati, Lindsey Gray, Thomas Klijnsma, Jim Kowalkowski, Panagiotis Spentzouris, Jean-Roch Vlimant, Maria Spiropulu, Adam Aurisano, Jeremy Hewes, Aristeidis Tsaris, Kazuhiro Terao, Tracy Usher

Detector information can be associated with nodes and edges, enabling a GNN to propagate the embedded parameters around the graph and predict node-, edge- and graph-level observables.

1 code implementation • 1 May 2020 • Chiyu Max Jiang, Soheil Esmaeilzadeh, Kamyar Azizzadenesheli, Karthik Kashinath, Mustafa Mustafa, Hamdi A. Tchelepi, Philip Marcus, Prabhat, Anima Anandkumar

We propose MeshfreeFlowNet, a novel deep learning-based super-resolution framework to generate continuous (grid-free) spatio-temporal solutions from the low-resolution inputs.

no code implementations • 25 Mar 2020 • Xiangyang Ju, Steven Farrell, Paolo Calafiura, Daniel Murnane, Prabhat, Lindsey Gray, Thomas Klijnsma, Kevin Pedro, Giuseppe Cerati, Jim Kowalkowski, Gabriel Perdue, Panagiotis Spentzouris, Nhan Tran, Jean-Roch Vlimant, Alexander Zlokapa, Joosep Pata, Maria Spiropulu, Sitong An, Adam Aurisano, Jeremy Hewes, Aristeidis Tsaris, Kazuhiro Terao, Tracy Usher

Pattern recognition problems in high energy physics are notably different from traditional machine learning applications in computer vision.

Instrumentation and Detectors High Energy Physics - Experiment Computational Physics Data Analysis, Statistics and Probability

no code implementations • ICLR Workshop DeepDiffEq 2019 • Chiyu "Max" Jiang, Karthik Kashinath, Prabhat, Philip Marcus

To this end, we propose the use of a novel differentiable spectral projection layer for neural networks that efficiently enforces spatial PDE constraints using spectral methods, yet is fully differentiable, allowing for its use as a layer within Convolutional Neural Networks (CNNs) during end-to-end training.

1 code implementation • ICLR 2020 • Chiyu "Max" Jiang, Karthik Kashinath, Prabhat, Philip Marcus

To this end, we propose the use of a novel differentiable spectral projection layer for neural networks that efficiently enforces spatial PDE constraints using spectral methods, yet is fully differentiable, allowing for its use as a layer in neural networks that supports end-to-end training.

no code implementations • 29 Oct 2019 • Liu Yang, Sean Treichler, Thorsten Kurth, Keno Fischer, David Barajas-Solano, Josh Romero, Valentin Churavy, Alexandre Tartakovsky, Michael Houston, Prabhat, George Karniadakis

Uncertainty quantification for forward and inverse problems is a central challenge across physical and biomedical disciplines.

2 code implementations • 25 Sep 2019 • Adam Rupe, Nalini Kumar, Vladislav Epifanov, Karthik Kashinath, Oleksandr Pavlyk, Frank Schlimbach, Mostofa Patwary, Sergey Maidanov, Victor Lee, Prabhat, James P. Crutchfield

Extracting actionable insight from complex unlabeled scientific data is an open challenge and key to unlocking data-driven discovery in science.

no code implementations • 16 Sep 2019 • Adam Rupe, Karthik Kashinath, Nalini Kumar, Victor Lee, Prabhat, James P. Crutchfield

Extreme weather is one of the main mechanisms through which climate change will directly impact human society.

3 code implementations • 8 Jul 2019 • Atılım Güneş Baydin, Lei Shao, Wahid Bhimji, Lukas Heinrich, Lawrence Meadows, Jialin Liu, Andreas Munk, Saeid Naderiparizi, Bradley Gram-Hansen, Gilles Louppe, Mingfei Ma, Xiaohui Zhao, Philip Torr, Victor Lee, Kyle Cranmer, Prabhat, Frank Wood

Probabilistic programming languages (PPLs) are receiving widespread attention for performing Bayesian inference in complex generative models.

no code implementations • 13 May 2019 • Jin-Long Wu, Karthik Kashinath, Adrian Albert, Dragos Chirila, Prabhat, Heng Xiao

In this work, we present a statistical constrained generative adversarial network by enforcing constraints of covariance from the training data, which results in an improved machine-learning-based emulator to capture the statistics of the training data generated by solving fully resolved PDEs.

1 code implementation • ICLR 2019 • Chiyu "Max" Jiang, Jingwei Huang, Karthik Kashinath, Prabhat, Philip Marcus, Matthias Niessner

We present an efficient convolution kernel for Convolutional Neural Networks (CNNs) on unstructured grids using parameterized differential operators while focusing on spherical signals such as panorama images or planetary signals.

Ranked #24 on Semantic Segmentation on Stanford2D3D Panoramic

3 code implementations • 14 Oct 2018 • Steven Farrell, Paolo Calafiura, Mayur Mudigonda, Prabhat, Dustin Anderson, Jean-Roch Vlimant, Stephan Zheng, Josh Bendavid, Maria Spiropulu, Giuseppe Cerati, Lindsey Gray, Jim Kowalkowski, Panagiotis Spentzouris, Aristeidis Tsaris

The second set of models use Graph Neural Networks (GNNs) for the tasks of hit classification and segment classification.

High Energy Physics - Experiment Data Analysis, Statistics and Probability

3 code implementations • 3 Oct 2018 • Thorsten Kurth, Sean Treichler, Joshua Romero, Mayur Mudigonda, Nathan Luehr, Everett Phillips, Ankur Mahesh, Michael Matheson, Jack Deslippe, Massimiliano Fatica, Prabhat, Michael Houston

The Tiramisu network scales to 5300 P100 GPUs with a sustained throughput of 21. 0 PF/s and parallel efficiency of 79. 0%.

Distributed, Parallel, and Cluster Computing

1 code implementation • 17 Sep 2018 • Nicholas Choma, Federico Monti, Lisa Gerhardt, Tomasz Palczewski, Zahra Ronaghi, Prabhat, Wahid Bhimji, Michael M. Bronstein, Spencer R. Klein, Joan Bruna

Tasks involving the analysis of geometric (graph- and manifold-structured) data have recently gained prominence in the machine learning community, giving birth to a rapidly developing field of geometric deep learning.

no code implementations • 21 Aug 2018 • Mahesh Balasubramanian, Trevor Ruiz, Brandon Cook, Sharmodeep Bhattacharyya, Prabhat, Aviral Shrivastava, Kristofer Bouchard

The analysis of scientific data of increasing size and complexity requires statistical machine learning methods that are both interpretable and predictive.

no code implementations • 14 Aug 2018 • Amrita Mathuriya, Deborah Bard, Peter Mendygral, Lawrence Meadows, James Arnemann, Lei Shao, Siyu He, Tuomas Karna, Daina Moise, Simon J. Pennycook, Kristyn Maschoff, Jason Sewall, Nalini Kumar, Shirley Ho, Mike Ringenburg, Prabhat, Victor Lee

Deep learning is a promising tool to determine the physical model that describes our universe.

3 code implementations • NeurIPS 2019 • Atılım Güneş Baydin, Lukas Heinrich, Wahid Bhimji, Lei Shao, Saeid Naderiparizi, Andreas Munk, Jialin Liu, Bradley Gram-Hansen, Gilles Louppe, Lawrence Meadows, Philip Torr, Victor Lee, Prabhat, Kyle Cranmer, Frank Wood

We present a novel probabilistic programming framework that couples directly to existing large-scale simulators through a cross-platform probabilistic execution protocol, which allows general-purpose inference engines to record and control random number draws within simulators in a language-agnostic way.

1 code implementation • 28 Feb 2018 • Jeffrey Regier, Andrew C. Miller, David Schlegel, Ryan P. Adams, Jon D. McAuliffe, Prabhat

We present a new, fully generative model for constructing astronomical catalogs from optical telescope image sets.

1 code implementation • 31 Jan 2018 • Jeffrey Regier, Kiran Pamnany, Keno Fischer, Andreas Noack, Maximilian Lam, Jarrett Revels, Steve Howard, Ryan Giordano, David Schlegel, Jon McAuliffe, Rollin Thomas, Prabhat

We construct an astronomical catalog from 55 TB of imaging data using Celeste, a Bayesian variational inference code written entirely in the high-productivity programming language Julia.

Distributed, Parallel, and Cluster Computing Instrumentation and Methods for Astrophysics 85A35, 68W10, 62P35 J.2; D.1.3; G.3; I.2; D.2

no code implementations • 21 Dec 2017 • Mario Lezcano Casado, Atilim Gunes Baydin, David Martinez Rubio, Tuan Anh Le, Frank Wood, Lukas Heinrich, Gilles Louppe, Kyle Cranmer, Karen Ng, Wahid Bhimji, Prabhat

We consider the problem of Bayesian inference in the family of probabilistic models implicitly defined by stochastic generative models of data.

5 code implementations • 9 Nov 2017 • Wahid Bhimji, Steven Andrew Farrell, Thorsten Kurth, Michela Paganini, Prabhat, Evan Racah

There has been considerable recent activity applying deep convolutional neural nets (CNNs) to data from particle physics experiments.

no code implementations • 17 Aug 2017 • Thorsten Kurth, Jian Zhang, Nadathur Satish, Ioannis Mitliagkas, Evan Racah, Mostofa Ali Patwary, Tareq Malas, Narayanan Sundaram, Wahid Bhimji, Mikhail Smorkalov, Jack Deslippe, Mikhail Shiryaev, Srinivas Sridharan, Prabhat, Pradeep Dubey

This paper presents the first, 15-PetaFLOP Deep Learning system for solving scientific pattern classification problems on contemporary HPC architectures.

no code implementations • NeurIPS 2017 • Kristofer E. Bouchard, Alejandro F. Bujan, Farbod Roosta-Khorasani, Shashanka Ubaru, Prabhat, Antoine M. Snijders, Jian-Hua Mao, Edward F. Chang, Michael W. Mahoney, Sharmodeep Bhattacharyya

The increasing size and complexity of scientific data could dramatically enhance discovery and prediction for basic scientific applications.

1 code implementation • NeurIPS 2017 • Evan Racah, Christopher Beckham, Tegan Maharaj, Samira Ebrahimi Kahou, Prabhat, Christopher Pal

We present a dataset, ExtremeWeather, to encourage machine learning research in this area and to help facilitate further work in understanding and mitigating the effects of climate change.

no code implementations • 10 Nov 2016 • Jeffrey Regier, Kiran Pamnany, Ryan Giordano, Rollin Thomas, David Schlegel, Jon McAuliffe, Prabhat

Celeste is a procedure for inferring astronomical catalogs that attains state-of-the-art scientific results.

1 code implementation • 5 Jul 2016 • Alex Gittens, Aditya Devarakonda, Evan Racah, Michael Ringenburg, Lisa Gerhardt, Jey Kottalam, Jialin Liu, Kristyn Maschhoff, Shane Canon, Jatin Chhugani, Pramod Sharma, Jiyan Yang, James Demmel, Jim Harrell, Venkat Krishnamurthy, Michael W. Mahoney, Prabhat

We explore the trade-offs of performing linear algebra using Apache Spark, compared to traditional C and MPI implementations on HPC platforms.

Distributed, Parallel, and Cluster Computing G.1.3; C.2.4

no code implementations • 4 May 2016 • Yunjie Liu, Evan Racah, Prabhat, Joaquin Correa, Amir Khosrowshahi, David Lavers, Kenneth Kunkel, Michael Wehner, William Collins

Detecting extreme events in large datasets is a major challenge in climate science research.

no code implementations • 28 Jan 2016 • Evan Racah, Seyoon Ko, Peter Sadowski, Wahid Bhimji, Craig Tull, Sang-Yun Oh, Pierre Baldi, Prabhat

Experiments in particle physics produce enormous quantities of data that must be analyzed and interpreted by teams of physicists.

no code implementations • 3 Jun 2015 • Jeffrey Regier, Andrew Miller, Jon McAuliffe, Ryan Adams, Matt Hoffman, Dustin Lang, David Schlegel, Prabhat

We present a new, fully generative model of optical telescope image sets, along with a variational procedure for inference.

4 code implementations • 19 Feb 2015 • Jasper Snoek, Oren Rippel, Kevin Swersky, Ryan Kiros, Nadathur Satish, Narayanan Sundaram, Md. Mostofa Ali Patwary, Prabhat, Ryan P. Adams

Bayesian optimization is an effective methodology for the global optimization of functions with expensive evaluations.

Ranked #157 on Image Classification on CIFAR-100 (using extra training data)

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