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We use artificial intelligence (AI) to learn and infer the physics of higherorder gravitational wave modes of quasi-circular, spinning, non precessingbinary black hole mergers. We trained AI models using 14 million waveforms,produced with the surrogate model NRHybSur3dq8, that include modes up to $\ell\leq 4$ and $(5,5)$, except for $(4,0)$ and $(4,1)$, that describe binarieswith mass-ratios $q\leq8$, individual spins $s^z_{\{1,2\}}\in[-0.8, 0.8]$, andinclination angle $\theta\in[0,\pi]$.Our probabilistic AI surrogates canaccurately constrain the mass-ratio, individual spins, effective spin, andinclination angle of numerical relativity waveforms that describe such signalmanifold. We compared the predictions of our AI models with Gaussian processregression, random forest, k-nearest neighbors, and linear regression, and withtraditional Bayesian inference methods through the PyCBC Inference toolkit,finding that AI outperforms all these approaches in terms of accuracy, and arebetween three to four orders of magnitude faster than traditional Bayesianinference methods. Our AI surrogates were trained within 3.4 hours usingdistributed training on 1,536 NVIDIA V100 GPUs in the Summit supercomputer.

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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