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

arXiv: https://arxiv.org/abs/2110.06968 · HTML: https://mlsanity.com/paper/2110.06968/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 42 references that are in the map.
- First posted on arXiv 13 Oct 2021.

## Builds on (most cited first, 21 of 21)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Physics-Constrained Deep Learning for High-dimensional Surrogate Modeling and Uncertainty Quantification without Labeled Data](https://mlsanity.com/paper/1901.06314/) (2019)
- [Real-time gravitational-wave science with neural posterior estimation](https://mlsanity.com/paper/2106.12594/) (2021)
- [Bayesian parameter estimation using conditional variational autoencoders for gravitational-wave astronomy](https://mlsanity.com/paper/1909.06296/) (2019)
- [Deep Learning for Real-time Gravitational Wave Detection and Parameter Estimation: Results with Advanced LIGO Data](https://mlsanity.com/paper/1711.03121/) (2017)
- [Complete parameter inference for GW150914 using deep learning](https://mlsanity.com/paper/2008.03312/) (2020)
- [Deep Neural Networks to Enable Real-time Multimessenger Astrophysics](https://mlsanity.com/paper/1701.00008/) (2016)
- [Accelerated, Scalable and Reproducible AI-driven Gravitational Wave Detection](https://mlsanity.com/paper/2012.08545/) (2020)
- [Gravitational-wave parameter estimation with autoregressive neural network flows](https://mlsanity.com/paper/2002.07656/) (2020)
- [Learning Bayesian posteriors with neural networks for gravitational-wave inference](https://mlsanity.com/paper/1909.05966/) (2019)
- [Enabling real-time multi-messenger astrophysics discoveries with deep learning](https://mlsanity.com/paper/1911.11779/) (2019)
- [Statistically-informed deep learning for gravitational wave parameter estimation](https://mlsanity.com/paper/1903.01998/) (2019)
- [Improved Surrogates in Inertial Confinement Fusion with Manifold and Cycle Consistencies](https://mlsanity.com/paper/1912.08113/) (2019)
- [Denoising Gravitational Waves with Enhanced Deep Recurrent Denoising Auto-Encoders](https://mlsanity.com/paper/1903.03105/) (2019)
- [Advances in Machine and Deep Learning for Modeling and Real-time Detection of Multi-Messenger Sources](https://mlsanity.com/paper/2105.06479/) (2021)
- [Reduced-order modeling with artificial neurons for gravitational-wave inference](https://mlsanity.com/paper/1811.05491/) (2018)
- [Detection of gravitational-wave signals from binary neutron star mergers using machine learning](https://mlsanity.com/paper/2006.01509/) (2020)
- [Detection of Gravitational Waves Using Bayesian Neural Networks](https://mlsanity.com/paper/2007.04176/) (2020)
- [Genetic-algorithm-optimized neural networks for gravitational wave classification](https://mlsanity.com/paper/2010.04340/) (2020)
- [Physics-inspired deep learning to characterize the signal manifold of quasi-circular, spinning, non-precessing binary black hole mergers](https://mlsanity.com/paper/2004.09524/) (2020)
- [From One to Many: A Deep Learning Coincident Gravitational-Wave Search](https://mlsanity.com/paper/2108.10715/) (2021)

## Built on by (most cited first, 5 of 5)

- [Applications of physics informed neural operators](https://mlsanity.com/paper/2203.12634/) (2022)
- [Inference-optimized AI and high performance computing for gravitational wave detection at scale](https://mlsanity.com/paper/2201.11133/) (2022)
- [FAIR AI Models in High Energy Physics](https://mlsanity.com/paper/2212.05081/) (2022)
- [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](https://mlsanity.com/paper/2112.07669/) (2021)
- [NeoSySPArtaN: A Neuro-Symbolic Spin Prediction Architecture for higher-order multipole waveforms from eccentric Binary Black Hole mergers using Numerical Relativity](https://mlsanity.com/paper/2307.11003/) (2023)

## Abstract (arXiv)

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. We used the NRHybSur3dq8 surrogate model to produce train, validation and test sets of ℓ=|m|=2 waveforms that cover the parameter space of binary black hole mergers with mass-ratios q≤8 and individual spins |sz_1,2| ≤ 0.8. These waveforms cover the time range t∈[-5000textrmM, 130textrmM], where t=0M marks the merger event, defined as the maximum value of the waveform amplitude. We harnessed the ThetaGPU supercomputer at the Argonne Leadership Computing Facility to train our AI model using a training set of 1.5 million waveforms. We used 16 NVIDIA DGX A100 nodes, each consisting of 8 NVIDIA A100 Tensor Core GPUs and 2 AMD Rome CPUs, to fully train our model within 3.5 hours. Our findings show that artificial intelligence can accurately forecast the dynamical evolution of numerical relativity waveforms in the time range t∈[-100textrmM, 130textrmM]. Sampling a test set of 190,000 waveforms, we find that the average overlap between target and predicted waveforms is gtrsim99% over the entire parameter space under consideration. We also combined scientific visualization and accelerated computing to identify what components of our model take in knowledge from the early and late-time waveform evolution to accurately forecast the latter part of numerical relativity waveforms. This work aims to accelerate the creation of scalable, computationally efficient and interpretable artificial intelligence models for gravitational wave astrophysics.