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Interpreting a Machine Learning Model for Detecting Gravitational Waves


Feb 15, 2022
Mohammadtaher Safarzadeh, Asad Khan, E. A. Huerta, Martin Wattenberg

* 19 pages, to be submitted, comments are welcome. Movies based on this work can be accessed via: https://www.youtube.com/watch?v=SXFGMOtJwn0 https://www.youtube.com/watch?v=itVCj9gpmAs 

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Inference-optimized AI and high performance computing for gravitational wave detection at scale


Jan 26, 2022
Pranshu Chaturvedi, Asad Khan, Minyang Tian, E. A. Huerta, Huihuo Zheng

* 19 pages, 8 figure 

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AI and extreme scale computing to learn and infer the physics of higher order gravitational wave modes of quasi-circular, spinning, non-precessing binary black hole mergers


Dec 13, 2021
Asad Khan, E. A. Huerta

* 21 pages, 12 figures 

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Interpretable AI forecasting for numerical relativity waveforms of quasi-circular, spinning, non-precessing binary black hole mergers


Oct 13, 2021
Asad Khan, E. A. Huerta, Huihuo Zheng

* 17 pages, 7 figures, 1 appendix 

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Confluence of Artificial Intelligence and High Performance Computing for Accelerated, Scalable and Reproducible Gravitational Wave Detection


Dec 15, 2020
E. A. Huerta, Asad Khan, Xiaobo Huang, Minyang Tian, Maksim Levental, Ryan Chard, Wei Wei, Maeve Heflin, Daniel S. Katz, Volodymyr Kindratenko, Dawei Mu, Ben Blaiszik, Ian Foster

* 17 pages, 5 figures 

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Physics-inspired deep learning to characterize the signal manifold of quasi-circular, spinning, non-precessing binary black hole mergers


Apr 20, 2020
Asad Khan, E. A. Huerta, Arnav Das

* 21 pages, 10 figures, 1 appendix, 1 Interactive visualization at https://khanx169.github.io/smr_bbm_v2/interactive_results.html 

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Convergence of Artificial Intelligence and High Performance Computing on NSF-supported Cyberinfrastructure


Mar 18, 2020
E. A. Huerta, Asad Khan, Edward Davis, Colleen Bushell, William D. Gropp, Daniel S. Katz, Volodymyr Kindratenko, Seid Koric, William T. C. Kramer, Brendan McGinty, Kenton McHenry, Aaron Saxton

* White paper accepted to the NSF Workshop on Smart Cyberinfrastructure, February 25-27, 2020 http://smartci.sci.utah.edu/ 

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Enabling real-time multi-messenger astrophysics discoveries with deep learning


Nov 26, 2019
E. A. Huerta, Gabrielle Allen, Igor Andreoni, Javier M. Antelis, Etienne Bachelet, Bruce Berriman, Federica Bianco, Rahul Biswas, Matias Carrasco, Kyle Chard, Minsik Cho, Philip S. Cowperthwaite, Zachariah B. Etienne, Maya Fishbach, Francisco Förster, Daniel George, Tom Gibbs, Matthew Graham, William Gropp, Robert Gruendl, Anushri Gupta, Roland Haas, Sarah Habib, Elise Jennings, Margaret W. G. Johnson, Erik Katsavounidis, Daniel S. Katz, Asad Khan, Volodymyr Kindratenko, William T. C. Kramer, Xin Liu, Ashish Mahabal, Zsuzsa Marka, Kenton McHenry, Jonah Miller, Claudia Moreno, Mark Neubauer, Steve Oberlin, Alexander R. Olivas, Donald Petravick, Adam Rebei, Shawn Rosofsky, Milton Ruiz, Aaron Saxton, Bernard F. Schutz, Alex Schwing, Ed Seidel, Stuart L. Shapiro, Hongyu Shen, Yue Shen, Leo Singer, Brigitta M. Sipőcz, Lunan Sun, John Towns, Antonios Tsokaros, Wei Wei, Jack Wells, Timothy J. Williams, Jinjun Xiong, Zhizhen Zhao

* Nature Reviews Physics volume 1, pages 600-608 (2019) 
* Invited Expert Recommendation for Nature Reviews Physics. The art work produced by E. A. Huerta and Shawn Rosofsky for this article was used by Carl Conway to design the cover of the October 2019 issue of Nature Reviews Physics 

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Robotic Hierarchical Graph Neurons. A novel implementation of HGN for swarm robotic behaviour control


Oct 28, 2019
Phillip Smith, Aldeida Aleti, Vincent C. S. Lee, Robert Hunjet, Asad Khan


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Swarm Behaviour Evolution via Rule Sharing and Novelty Search


Oct 28, 2019
Phillip Smith, Robert Hunjet, Aldeida Aleti, Asad Khan


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