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We present a deep-learning artificial intelligence model that is capable of learning and forecasting the late-inspiral, merger and ringdown of numerical relativity waveforms that describe quasi-circular, spinning, non-precessing binary black hole mergers.
2017
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D. George and E. A. Huerta, “Deep neural networks to enable real-time multimessenger astrophysics,” Phys. Rev. D
2018
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D. George and E. Huerta, “Deep learning for real-time gravitational wave detection and parameter estimation: Results with advanced ligo data,” Physics Letters B
2018
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2018
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2019
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2019
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A. Rebei, E. A. Huerta, S. Wang, S. Habib, R. Haas, D. Johnson, and D. George, “Fusing numerical relativity and deep learning to detect higher-order multipole waveforms from eccentric binary black hole mergers,” Phys. Rev. D
2019
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2019
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H. Shen, D. George, E. A. Huerta, and Z. Zhao, “Denoising gravitational waves with enhanced deep recurrent denoising auto-encoders,” in ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
2019
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A. J. K. Chua, C. R. Galley, and M. Vallisneri, “Reduced-order modeling with artificial neurons for gravitational-wave inference,” Phys. Rev. Lett
2019
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2019
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Y. Zhu, N. Zabaras, P.-S. Koutsourelakis, and P. Perdikaris, “Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data,” Journal of Computational Physics
2019
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V. Varma, S. E. Field, M. A. Scheel, J. Blackman, L. E. Kidder, and H. P. Pfeiffer, “Surrogate model of hybridized numerical relativity binary black hole waveforms,” Phys. Rev. D
2019
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2020
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H. Wang, S. Wu, Z. Cao, X. Liu, and J.-Y. Zhu, “Gravitational-wave signal recognition of LIGO data by deep learning,” Phys. Rev. D
2020
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X.-R. Li, G. Babu, W.-L. Yu, and X.-L. Fan, “Some optimizations on detecting gravitational wave using convolutional neural network,” Front. Phys. (Beijing)
2020
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2020
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P. G. Krastev, “Real-Time Detection of Gravitational Waves from Binary Neutron Stars using Artificial Neural Networks,” Phys. Lett. B
2020
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M. B. Schäfer, F. Ohme, and A. H. Nitz, “Detection of gravitational-wave signals from binary neutron star mergers using machine learning,” Phys. Rev. D
2020
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Y.-C. Lin and J.-H. P. Wu, “Detection of gravitational waves using bayesian neural networks,” Phys. Rev. D
2021
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2021
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H. Shen, E. Huerta, E. O’Shea, P. Kumar, and Z. Zhao, “Statistically-informed deep learning for gravitational wave parameter estimation,” Machine Learning: Science and Technology
2021
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H. Gabbard, C. Messenger, I. S. Heng, F. Tonolini, and R. Murray-Smith, “Bayesian parameter estimation using conditional variational autoencoders for gravitational-wave astronomy,” Nature Physics
2021
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S. R. Green and J. Gair, “Complete parameter inference for gw150914 using deep learning,” Machine Learning: Science and Technology
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2020
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B. Beheshtipour and M. A. Papa, “Deep learning for clustering of continuous gravitational wave candidates,” Phys. Rev. D
2020
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W. Wei and E. A. Huerta, “Gravitational Wave Denoising of Binary Black Hole Mergers with Deep Learning,” Phys. Lett
2020
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R. Ormiston, T. Nguyen, M. Coughlin, R. X. Adhikari, and E. Katsavounidis, “Noise reduction in gravitational-wave data via deep learning,” Phys. Rev. Research
2020
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A. J. Chua and M. Vallisneri, “Learning Bayesian posteriors with neural networks for gravitational-wave inference,” Phys. Rev. Lett
2020
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S. R. Green, C. Simpson, and J. Gair, “Gravitational-wave parameter estimation with autoregressive neural network flows,” Phys. Rev. D
2020
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A. Khan, E. Huerta, and A. Das, “Physics-inspired deep learning to characterize the signal manifold of quasi-circular, spinning, non-precessing binary black hole mergers,” Physics Letters B
2020
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Singapore: Springer Singapore, 2020
E. A. Huerta and Z. Zhao, Advances in Machine and Deep Learning for Modeling and Real-Time Detection of Multi-messenger Sources · 2020
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2021
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M. Dax, S. R. Green, J. Gair, J. H. Macke, A. Buonanno, and B. Schölkopf, “Real-time gravitational wave science with neural posterior estimation,” Physical review letters
2021
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S. Khan and R. Green, “Gravitational-wave surrogate models powered by artificial neural networks,” Phys. Rev. D
2021
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W. Wei, E. A. Huerta, M. Yun, N. Loutrel, M. A. Shaikh, P. Kumar, R. Haas, and V. Kindratenko, “Deep Learning with Quantized Neural Networks for Gravitational-wave Forecasting of Eccentric Compact Binary Coalescence,” Astrophys. J
2021
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W. Wei and E. A. Huerta, “Deep learning for gravitational wave forecasting of neutron star mergers,” Phys. Lett. B
2021
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H. Yu, R. X. Adhikari, R. Magee, S. Sachdev, and Y. Chen, “Early warning of coalescing neutron-star and neutron-star-black-hole binaries from the nonstationary noise background using neural networks,” Phys. Rev. D
2021
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W. Wei, A. Khan, E. A. Huerta, X. Huang, and M. Tian, “Deep learning ensemble for real-time gravitational wave detection of spinning binary black hole mergers,” Physics Letters B
2021
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E. Huerta, A. Khan, X. Huang, M. Tian, M. Levental, R. Chard, W. Wei, M. Heflin, D. S. Katz, V. Kindratenko, et al
2021
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J. Lee, S. H. Oh, K. Kim, G. Cho, J. J. Oh, E. J. Son, and H. M. Lee, “Deep learning model on gravitational waveforms in merging and ringdown phases of binary black hole coalescences,” Phys. Rev. D
2021
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[Online from October 2021]
A. Khan, E. A. Huerta, and H. Zheng, “Interpretable AI forecasting for numerical relativity waveforms of quasi-circular, spinning, non-precessing binary black hole mergers.” https://khanx169.github.io/gw_forecasting/interactive_results.html , 2021 · 2021
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