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

• URL: http://arxiv.org/abs/2506.19679v1
• Date: Tue, 24 Jun 2025 14:46:23 GMT
• Title: Extreme Learning Machines for Exoplanet Simulations: A Faster, Lightweight Alternative to Deep Learning
• Authors: Tara P. A. Tahseen, Luís F. Simões, Kai Hou Yip, Nikolaos Nikolaou, João M. Mendonça, Ingo P. Waldmann,
• Abstract summary: Extreme Learning Machine (ELM) is a lightweight, non-gradient-based machine learning algorithm for accelerating complex physical models.<n>We evaluate ELM surrogate models in two test cases with different data structures.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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