Extreme Learning Machines for Exoplanet Simulations: A Faster, Lightweight Alternative to Deep Learning

TL;DR

This paper demonstrates that Extreme Learning Machines (ELMs) can serve as fast, lightweight surrogates for complex physical models in astrophysics, outperforming traditional neural networks in training speed and efficiency.

Contribution

It introduces ELMs as an efficient alternative to deep learning for simulating astrophysical data, showing their effectiveness across different data structures and sizes.

Findings

ELMs achieve 100,000× faster training than BIRNN in sequential data.

An ensemble of ELMs matches CNN accuracy with 16.4× less training time in image data.

ELMs require significantly less data than traditional neural networks for comparable performance.

Abstract

Increasing resolution and coverage of astrophysical and climate data necessitates increasingly sophisticated models, often pushing the limits of computational feasibility. While emulation methods can reduce calculation costs, the neural architectures typically used--optimised via gradient descent--are themselves computationally expensive to train, particularly in terms of data generation requirements. This paper investigates the utility of the Extreme Learning Machine (ELM) as a lightweight, non-gradient-based machine learning algorithm for accelerating complex physical models. We evaluate ELM surrogate models in two test cases with different data structures: (i) sequentially-structured data, and (ii) image-structured data. For test case (i), where the number of samples $N$ >> the dimensionality of input data $d$, ELMs achieve remarkable efficiency, offering a 100,000$\times$ faster training time and a 40$\times$ faster prediction speed compared to a Bi-Directional Recurrent Neural Network (BIRNN), whilst improving upon BIRNN test performance. For test case (ii), characterised by $d >> N$ and image-based inputs, a single ELM was insufficient, but an ensemble of 50 individual ELM predictors achieves comparable accuracy to a benchmark Convolutional Neural Network (CNN), with a 16.4$\times$ reduction in training time, though costing a 6.9$\times$ increase in prediction time. We find different sample efficiency characteristics between the test cases: in test case (i) individual ELMs demonstrate superior sample efficiency, requiring only 0.28% of the training dataset compared to the benchmark BIRNN, while in test case (ii) the ensemble approach requires 78% of the data used by the CNN to achieve comparable results--representing a trade-off between sample efficiency and model complexity.