Fast, accurate, and differentiable: a neural-network surrogate for NRSur7dq4 precessing binary black hole waveforms

• URL: http://arxiv.org/abs/2607.24960v1
• Date: Mon, 27 Jul 2026 18:08:33 GMT
• Title: Fast, accurate, and differentiable: a neural-network surrogate for NRSur7dq4 precessing binary black hole waveforms
• Abstract summary: We present a neural network surrogate model that emulates the NRSur7dq4 gravitational waveform model.<n>We validate the surrogate against NRSur7dq4 on 10,000 waveforms spanning its full parameter space.<n>This is the first neural-network surrogate of a precessing numerical-relativity waveform model to combine validated NR-faithful accuracy with a fully differentiable, GPU-accelerated inference pipeline.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a neural network surrogate model that emulates the NRSur7dq4 gravitational waveform model for precessing binary black hole mergers. The surrogate decomposes the waveform into constituent quantities and trains an independent multilayer perceptron (MLP) for each. We validate the surrogate against NRSur7dq4 on 10,000 waveforms spanning its full parameter space ($1 \leq q \leq 4$, $|χ_{A,B}| \leq 0.8$). For representative total masses between 60 and 300 $M_\odot$, median sky-averaged frequency-domain mismatches range from $8.0 \times 10^{-5}$ to $1.7 \times 10^{-4}$, with 95th percentiles below $10^{-3}$. On an NVIDIA L40S GPU the JAX surrogate evaluates a single waveform in about 1 ms end-to-end, roughly 10 times faster than the LALSimulation C implementation of NRSur7dq4, and sustains about 140 times the LALSimulation throughput at batch size 64, making it well suited for both low-latency parameter-estimation samplers and large-scale waveform generation. The full NRSur7dq4 NN waveform-to-likelihood pipeline is implemented in JAX and is differentiable. This is the first neural-network surrogate of a precessing numerical-relativity waveform model to combine validated NR-faithful accuracy with a fully differentiable, GPU-accelerated inference pipeline, enabling gradient-based inference approaches via automatic differentiation including Fisher information matrices, GPU-accelerated nested sampling, gradient-based MCMC and importance sampling.

Related papers

• ROMNet: a hybrid reduced order modeling and machine learning approach to waveform inversion [53.79823394212125]
  Waveform inversion seeks to estimate the wave speed of a heterogeneous, inaccessible medium.<n>We introduce the methodology, called ROMNet, and test it with numerical simulations.<n>We compare the performance of ROMNet with the direct ROM-based inversion and with two representative deep learning approaches to FWI.
  arXiv  Detail & Related papers  (2026-08-25T21:13:39Z)
• Closed-Form Spectral Regularization for Multi-Task Model Merging [96.82449201305234]
  Model merging combines several independently fine-tuned experts into a single multi-task model without any training data.<n>State-of-the-art merging methods formulate merging as a layer-wise interference problem.<n>We propose SWUDI, a closed-form method that combines a soft exponential filter, which matches the gradient-flow trajectory of iterative descent.
  arXiv  Detail & Related papers  (2026-06-05T14:00:47Z)
• Entropy Across the Bridge: Conditional-Marginal Discretization for Flow and Schrödinger Samplers [68.9946633641494]
  Flow matching and Schrdinger bridges define probability, yet their inference grids are usually or inherited from one-endpoint diffusion.<n>We derive a conditional-marginal entropy-rate objective for bridge-aware discretization, separating endpoint-conditioned bridge geometry from marginal flow evolution.<n>On EDM/CIFAR-10, the entropic time-discretization gives the best tested five-step FID (186.3 pm 4.0 versus 200.5 pm 2.9 for linear and 238.0 pm 5.3 for cosine)
  arXiv  Detail & Related papers  (2026-05-15T16:11:10Z)
• LiQSS: Post-Transformer Linear Quantum-Inspired State-Space Tensor Networks for Real-Time 6G [85.58816960936069]
  Proactive and agentic control in Sixth-Generation (6G) Open Radio Access Networks (O-RAN) requires control-grade prediction under stringent Near-Time (Near-RT) latency and computational constraints.<n>This paper investigates a post-Transformer paradigm for efficient radio telemetry forecasting.<n>We propose a quantum-inspired state-space tensor network that replaces self-attention with stable structured state-space dynamics kernels.
  arXiv  Detail & Related papers  (2026-01-18T12:08:38Z)
• Auto-encoder model for faster generation of effective one-body gravitational waveform approximations [0.8602553195689513]
  We present a conditional variational auto-encoder model for faster generation of aligned-spin SEOBNRv4 inspiral-ring-down waveforms.<n>Our model is able to generate 100 waveforms in 0.1 second at an average speed of 4.46 ms per waveform.<n>This is 2-3 orders of magnitude faster than the native SEOBNRv4 implementation in lalsimulation.
  arXiv  Detail & Related papers  (2025-11-16T15:18:37Z)
• FFT-Accelerated Auxiliary Variable MCMC for Fermionic Lattice Models: A Determinant-Free Approach with $O(N\log N)$ Complexity [52.3171766248012]
  We introduce a Markov Chain Monte Carlo (MCMC) algorithm that dramatically accelerates the simulation of quantum many-body systems.<n>We validate our algorithm on benchmark quantum physics problems, accurately reproducing known theoretical results.<n>Our work provides a powerful tool for large-scale probabilistic inference and opens avenues for physics-inspired generative models.
  arXiv  Detail & Related papers  (2025-10-13T07:57:21Z)
• PINGS: Physics-Informed Neural Network for Fast Generative Sampling [0.0]
  We introduce PINGS, a framework that amortizes diffusion sampling by training a physics-informed network to approximate reverse-time probability-flow dynamics.<n>As a proof of concept, we learn a direct map from a 3D standard normal to a non-Gaussian Gaussian Mixture Model (GMM)<n>PINGS frames generative sampling as a PINN-style residual problem with endpoint anchoring, yielding a white-box, differentiable map with NFE = 1.
  arXiv  Detail & Related papers  (2025-09-14T14:22:33Z)
• Machine learning-driven conservative-to-primitive conversion in hybrid piecewise polytropic and tabulated equations of state [0.1572025118388268]
  We present a novel machine learning (ML) method to accelerate conservative-to-primitive inversion in hydrodynamics simulations.<n>We employ feedforward neural networks (NNC2PS and NNC2PL), trained in PyTorch, and optimized for GPU inference using NVIDIART.<n>The mixed-precisionRT engine for NNC2PS inference speeds approximately 400 times faster than a traditional single-threaded implementation for a dataset size of 1,000,000 points.
  arXiv  Detail & Related papers  (2024-12-10T19:00:01Z)
• A Deep Learning Powered Numerical Relativity Surrogate for Binary Black Hole Waveforms [32.776709911649554]
  We present a 2-stage training approach for neural network-based NR surrogate models.<n>These dual-stage artificial neural surrogate models offer rapid and competitively accurate waveform generation.<n>We show they can be used for parameter estimation tasks.
  arXiv  Detail & Related papers  (2024-12-09T19:45:30Z)
• Sequence modeling of higher-order wave modes of binary black hole mergers [0.0]
  Higher-order gravitational wave modes from quasi-circular, spinning, non-precessing binary black hole mergers encode key information about these systems' nonlinear dynamics.<n>We model these waveforms using transformer architectures, targeting the evolution from late inspiral through ringdown.<n>Our results demonstrate that transformer-based models can capture the nonlinear dynamics of binary black hole mergers with high accuracy, even outside the surrogate training domain.
  arXiv  Detail & Related papers  (2024-09-05T18:00:11Z)
• Neural Inference of Gaussian Processes for Time Series Data of Quasars [72.79083473275742]
  We introduce a new model that enables it to describe quasar spectra completely. We also introduce a new method of inference of Gaussian process parameters, which we call $textitNeural Inference$. The combination of both the CDRW model and Neural Inference significantly outperforms the baseline DRW and MLE.
  arXiv  Detail & Related papers  (2022-11-17T13:01:26Z)
• WaveFit: An Iterative and Non-autoregressive Neural Vocoder based on Fixed-Point Iteration [47.07494621683752]
  This study proposes a fast and high-quality neural vocoder called textitWaveFit. WaveFit integrates the essence of GANs into a DDPM-like iterative framework based on fixed-point iteration. Subjective listening tests showed no statistically significant differences in naturalness between human natural speech and those synthesized by WaveFit with five iterations.
  arXiv  Detail & Related papers  (2022-10-03T15:45:05Z)
• Minimum optical depth multiport interferometers for approximating arbitrary unitary operations and pure states [37.69303106863453]
  We address the problem of, using multiport interferometers, approximating with a given infidelity any pure state preparation and any unitary operation.<n>By means of numerical calculations, we show that pure states, in any dimension $d$, can be prepared with infidelity.
  arXiv  Detail & Related papers  (2020-02-04T15:40:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.