# Learning fluid physics from highly turbulent data using sparse physics-informed discovery of empirical relations (SPIDER)

@inproceedings{Gurevich2021LearningFP, title={Learning fluid physics from highly turbulent data using sparse physics-informed discovery of empirical relations (SPIDER)}, author={Daniel Gurevich and Patrick A. K. Reinbold and Roman O. Grigoriev}, year={2021} }

We show how a complete mathematical description of a complicated physical phenomenon can be learned from observational data via a hybrid approach combining three simple and general ingredients: physical assumptions of smoothness, locality, and symmetry, a weak formulation of diﬀerential equations, and sparse regression. To illustrate this, we extract a system of governing equations describing ﬂows of incompressible Newtonian ﬂuids – the Navier-Stokes equation, the continuity equation, and the…

## 3 Citations

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This paper demonstrates how machine learning can be leveraged to detect changes in global parameters from variations in an identified model using only observational data, and how this capability, when paired with first principles analysis, can effectively distinguish the effects of changing parameters from the intrinsic complexity of the system.

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