Details

Autor(en) / Beteiligte

• Reinbold, Patrick A. K.
• Kageorge, Logan M.
• Schatz, Michael F.
• Grigoriev, Roman O.

Titel

Robust learning from noisy, incomplete, high-dimensional experimental data via physically constrained symbolic regression

Ist Teil von

• Nature communications, 2021-05, Vol.12 (1), p.3219-3219, Article 3219

Ort / Verlag

London: Nature Publishing Group

Erscheinungsjahr

2021

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• Abstract 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.