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We use artificial intelligence (AI) to learn and infer the physics of higher order gravitational wave modes of quasi-circular, spinning, non precessing binary black hole mergers.
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2020
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2021
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2021
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2021
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E. A. Huerta, A. Khan, X. Huang, M. Tian, M. Levental, R. Chard, W. Wei, M. Heflin, D. S. Katz, V. Kindratenko, D. Mu, B. Blaiszik, and I. Foster, “Accelerated, scalable and reproducible AI-driven gravitational wave detection,” Nature Astronomy
2021
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2022
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2022
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A. Khan, E. A. Huerta, and H. Zheng, “Interpretable AI forecasting for numerical relativity waveforms of quasicircular, spinning, nonprecessing binary black hole mergers,” Phys. Rev. D
2022
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2022
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S. G. Rosofsky and E. A. Huerta, “Applications of physics informed neural operators,” arXiv e-prints
2022
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P. Chaturvedi, A. Khan, M. Tian, E. A. Huerta, and H. Zheng, “Inference-Optimized AI and High Performance Computing for Gravitational Wave Detection at Scale,” Front. Artif. Intell
2022
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