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Using Machine Learning to Augment Coarse-Grid Computational Fluid Dynamics Simulations


Oct 03, 2020
Jaideep Pathak, Mustafa Mustafa, Karthik Kashinath, Emmanuel Motheau, Thorsten Kurth, Marcus Day

* Corrected typographical errors in the previous version related to the incorrectly formatted accented character "\'e" appearing in various places in the manuscript 

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Hierarchical Roofline Performance Analysis for Deep Learning Applications


Sep 22, 2020
Yunsong Wang, Charlene Yang, Steven Farrell, Thorsten Kurth, Samuel Williams

* 9 pages 

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Time-Based Roofline for Deep Learning Performance Analysis


Sep 22, 2020
Yunsong Wang, Charlene Yang, Steven Farrell, Yan Zhang, Thorsten Kurth, Samuel Williams

* 9 pages 

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


Oct 29, 2019
Liu Yang, Sean Treichler, Thorsten Kurth, Keno Fischer, David Barajas-Solano, Josh Romero, Valentin Churavy, Alexandre Tartakovsky, Michael Houston, Prabhat, George Karniadakis

* 3rd Deep Learning on Supercomputers Workshop (DLS) at SC19 

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


Nov 29, 2017
Wahid Bhimji, Steven Andrew Farrell, Thorsten Kurth, Michela Paganini, Prabhat, Evan Racah

* Presented at ACAT 2017 Conference, Submitted to J. Phys. Conf. Ser 

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


Aug 17, 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

* 12 pages, 9 figures 

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