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Found 5 papers, 1 papers with code
Hyperparameter optimization of hp-greedy reduced basis for gravitational wave surrogates
In addition, we find that the Bayesian approach used in this paper for HPO is two orders of magnitude faster than, for example, a grid search, with about a $100 \times$ acceleration.
An automated parameter domain decomposition approach for gravitational wave surrogates using hp-greedy refinement
We introduce hp-greedy, a refinement approach for building gravitational wave surrogates as an extension of the standard reduced basis framework.
Gravitational wave surrogates through automated machine learning
We analyze a prospect for predicting gravitational waveforms from compact binaries based on automated machine learning (AutoML) from around a hundred different possible regression models, without having to resort to tedious and manual case-by-case analyses and fine-tuning.
Reduced Order and Surrogate Models for Gravitational Waves
We present an introduction to some of the state of the art in reduced order and surrogate modeling in gravitational wave (GW) science.
General Relativity and Quantum Cosmology Instrumentation and Methods for Astrophysics Numerical Analysis Numerical Analysis
Fast prediction and evaluation of gravitational waveforms using surrogate models
Our approach is based on three offline steps resulting in an accurate reduced-order model that can be used as a surrogate for the true/fiducial waveform family.
General Relativity and Quantum Cosmology Computational Engineering, Finance, and Science
