Reduced Order and Surrogate Models for Gravitational Waves

TL;DR

This paper reviews advanced reduced order and surrogate modeling techniques in gravitational wave science, focusing on methods for data compression, predictive modeling, and efficient data analysis, emphasizing practical, non-intrusive, and scalable approaches.

Contribution

It provides a comprehensive, self-contained overview of state-of-the-art reduced order and surrogate models in GW science, including practical algorithms and open challenges.

Findings

Survey of key modeling techniques like PCA, ROM, and Empirical Interpolation

Discussion on scalability, parallelization, and practical implementation

Identification of open challenges in high-dimensional surrogate modeling

Abstract

We present an introduction to some of the state of the art in reduced order and surrogate modeling in gravitational wave (GW) science. Approaches that we cover include Principal Component Analysis, Proper Orthogonal Decomposition, the Reduced Basis approach, the Empirical Interpolation Method, Reduced Order Quadratures, and Compressed Likelihood evaluations. We divide the review into three parts: representation/compression of known data, predictive models, and data analysis. The targeted audience is that one of practitioners in GW science, a field in which building predictive models and data analysis tools that are both accurate and fast to evaluate, especially when dealing with large amounts of data and intensive computations, are necessary yet can be challenging. As such, practical presentations and, sometimes, heuristic approaches are here preferred over rigor when the latter is not available. This review aims to be self-contained, within reasonable page limits, with little previous knowledge (at the undergraduate level) requirements in mathematics, scientific computing, and other disciplines. Emphasis is placed on optimality, as well as the curse of dimensionality and approaches that might have the promise of beating it. We also review most of the state of the art of GW surrogates. Some numerical algorithms, conditioning details, scalability, parallelization and other practical points are discussed. The approaches presented are to large extent non-intrusive and data-driven and can therefore be applicable to other disciplines. We close with open challenges in high dimension surrogates, which are not unique to GW science.