6 Papers
7 Citations
Wei Wei is an academic researcher from University of Illinois at Urbana–Champaign. The author has contributed to research in topics: Gravitational wave & Deep learning. The author has an hindex of 5, co-authored 6 publications.
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Papers
Enabling real-time multi-messenger astrophysics discoveries with deep learning
E. A. Huerta,Gabrielle Allen,Igor Andreoni,Javier M. Antelis,Etienne Bachelet,G. Bruce Berriman,Federica B. Bianco,Rahul Biswas,Matias Carrasco Kind,Kyle Chard,Minsik Cho,Philip S. Cowperthwaite,Zachariah B. Etienne,Maya Fishbach,F. Forster,D. George,Tom Gibbs,Matthew J. Graham,William Gropp,Robert A. Gruendl,Anushri Gupta,Roland Haas,Sarah Habib,Elise Jennings,M. W. G. Johnson,Erik Katsavounidis,Daniel S. Katz,Asad Khan,Volodymyr Kindratenko,William Kramer,Xin Liu,Ashish Mahabal,Zsuzsa Márka,Kenton McHenry,Jonah Miller,Claudia Moreno,Mark Neubauer,Steve Oberlin,A. Olivas,Don Petravick,Adam Rebei,Shawn Rosofsky,Milton Ruiz,Aaron Saxton,Bernard F. Schutz,Alexander G. Schwing,Edward Seidel,Stuart L. Shapiro,Hongyu Shen,Yue Shen,Leo Singer,Brigitta M. Sipocz,Lunan Sun,John Towns,Antonios Tsokaros,Wei Wei,Jack C. Wells,Timothy J. Williams,Jinjun Xiong,Zhizhen Zhao +59 more
- 03 Oct 2019
TL;DR: The key challenges of real-time observations of gravitational wave sources and their electromagnetic and astroparticle counterparts are reviewed, and a number of recommendations to maximize their potential for scientific discovery are made.
Deep learning for gravitational wave forecasting of neutron star mergers
Wei Wei,E.A. Huerta +1 more
TL;DR: In this paper, a deep learning time-series forecasting method was proposed for detecting binary neutron star mergers in real advanced LIGO data up to 30 seconds before the merger.
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Accelerated, Scalable and Reproducible AI-driven Gravitational Wave Detection
E. A. Huerta,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,Ben Blaiszik,Ian Foster,Ian Foster +15 more
TL;DR: A workflow that connects the Data and Learning Hub for Science, a repository for publishing AI models, with the Hardware Accelerated Learning (HAL) cluster, using funcX as a universal distributed computing service to open new pathways to conduct reproducible, accelerated, data-driven discovery.
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Accelerated, scalable and reproducible AI-driven gravitational wave detection
E. A. Huerta,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,Ben Blaiszik,Ian Foster,Ian Foster +15 more
TL;DR: In this paper, the authors developed a workflow that connects the Data and Learning Hub for Science, a repository for publishing AI models, with the Hardware-Accelerated Learning (HAL) cluster, using funcX as a universal distributed computing service.
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Deep learning ensemble for real-time gravitational wave detection of spinning binary black hole mergers
TL;DR: In this paper, a deep learning ensembles for real-time, gravitational wave detection of spinning binary black hole mergers is proposed, which consists of training independent neural networks that simultaneously process strain data from multiple detectors.