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Robust learning from noisy, incomplete, high-dimensional experimental data via physically constrained symbolic regression
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Robust learning from noisy, incomplete, high-dimensional experimental data via physically constrained symbolic regression
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Journal Article
Robust learning from noisy, incomplete, high-dimensional experimental data via physically constrained symbolic regression
2021
Overview
Machine learning offers an intriguing alternative to first-principle analysis for discovering new physics from experimental data. However, to date, purely data-driven methods have only proven successful in uncovering physical laws describing simple, low-dimensional systems with low levels of noise. Here we demonstrate that combining a data-driven methodology with some general physical principles enables discovery of a quantitatively accurate model of a non-equilibrium spatially extended system from high-dimensional data that is both noisy and incomplete. We illustrate this using an experimental weakly turbulent fluid flow where only the velocity field is accessible. We also show that this hybrid approach allows reconstruction of the inaccessible variables – the pressure and forcing field driving the flow.
Reinbold et al. propose a physics-informed data-driven approach that successfully discovers a dynamical model using high-dimensional, noisy and incomplete experimental data describing a weakly turbulent fluid flow. This approach is relevant to other non-equilibrium spatially-extended systems.
Publisher
Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
Subject
