Optimizing Neural Network Surrogate Models: Application to Black Hole Merger Remnants

TL;DR

This paper introduces a systematic optimization approach for neural network surrogate models of black hole merger simulations, significantly improving evaluation speed and providing insights into model tuning and physical process structure.

Contribution

It presents a formalized optimization strategy for neural network surrogates, applied to a black hole merger model, achieving speedups and better understanding of model settings.

Findings

Evaluation speedup of up to 8 times on CPU

Further 2000 times speedup on GPU in batch evaluations

Efficient training set size determination with iterative enrichment

Abstract

Surrogate models of numerical relativity simulations of merging black holes provide the most accurate tools for gravitational-wave data analysis. Neural network-based surrogates promise evaluation speedups, but their accuracy relies on (often obscure) tuning of settings such as the network architecture, hyperparameters, and the size of the training dataset. We propose a systematic optimization strategy that formalizes setting choices and motivates the amount of training data required. We apply this strategy on NRSur7dq4Remnant, an existing surrogate model for the properties of the remnant of generically precessing binary black hole mergers and construct a neural network version, which we label NRSur7dq4Remnant_NN. The systematic optimization strategy results in a new surrogate model with comparable accuracy, and provides insights into the meaning and role of the various network settings and hyperparameters as well as the structure of the physical process. Moreover, NRSur7dq4Remnant_NN results in evaluation speedups of up to $8$ times on a single CPU and a further improvement of $2,000$ times when evaluated in batches on a GPU. To determine the training set size, we propose an iterative enrichment strategy that efficiently samples the parameter space using much smaller training sets than naive sampling. NRSur7dq4Remnant_NN requires $O(10^4)$ training data, so neural network-based surrogates are ideal for speeding-up models that support such large training datasets, but at the moment cannot directly be applied to numerical relativity catalogs that are $O(10^3)$ in size. The optimization strategy is available through the gwbonsai package.