Affiliation:
1. Pasteur Labs, Brooklyn, NY 11205
2. Niels Bohr Institute, University of Copenhagen, Copenhagen 2100, Denmark
3. Department of Physics and Astronomy, University of Victoria, Victoria, BC V8W 2Y2, Canada
Abstract
Big data and large-scale machine learning have had a profound impact on science and engineering, particularly in fields focused on forecasting and prediction. Yet, it is still not clear how we can use the superior pattern-matching abilities of machine learning models for scientific discovery. This is because the goals of machine learning and science are generally not aligned. In addition to being accurate, scientific theories must also be causally consistent with the underlying physical process and allow for human analysis, reasoning, and manipulation to advance the field. In this paper, we present a case study on discovering a symbolic model for oceanic rogue waves from data using causal analysis, deep learning, parsimony-guided model selection, and symbolic regression. We train an artificial neural network on causal features from an extensive dataset of observations from wave buoys, while selecting for predictive performance and causal invariance. We apply symbolic regression to distill this black-box model into a mathematical equation that retains the neural network’s predictive capabilities, while allowing for interpretation in the context of existing wave theory. The resulting model reproduces known behavior, generates well-calibrated probabilities, and achieves better predictive scores on unseen data than current theory. This showcases how machine learning can facilitate inductive scientific discovery and paves the way for more accurate rogue wave forecasting.
Publisher
Proceedings of the National Academy of Sciences
Cited by
1 articles.
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1. Machine-guided discovery of a real-world rogue wave model;Proceedings of the National Academy of Sciences;2023-11-20