DeepAI AI Chat
Log In Sign Up

Shallow Learning for Fluid Flow Reconstruction with Limited Sensors and Limited Data

by   N. Benjamin Erichson, et al.
berkeley college

In many applications, it is important to reconstruct a fluid flow field, or some other high-dimensional state, from limited measurements and limited data. In this work, we propose a shallow neural network-based learning methodology for such fluid flow reconstruction. Our approach learns an end-to-end mapping between the sensor measurements and the high-dimensional fluid flow field, without any heavy preprocessing on the raw data. No prior knowledge is assumed to be available, and the estimation method is purely data-driven. We demonstrate the performance on three examples in fluid mechanics and oceanography, showing that this modern data-driven approach outperforms traditional modal approximation techniques which are commonly used for flow reconstruction. Not only does the proposed method show superior performance characteristics, it can also produce a comparable level of performance with traditional methods in the area, using significantly fewer sensors. Thus, the mathematical architecture is ideal for emerging global monitoring technologies where measurement data are often limited.


page 9

page 11

page 14

page 16

page 18

page 19

page 20


Tensor-based flow reconstruction from optimally located sensor measurements

Reconstructing high-resolution flow fields from sparse measurements is a...

Energy networks for state estimation with random sensors using sparse labels

State estimation is required whenever we deal with high-dimensional dyna...

Global field reconstruction from sparse sensors with Voronoi tessellation-assisted deep learning

Achieving accurate and robust global situational awareness of a complex ...

Flow based features and validation metric for machine learning reconstruction of PIV data

Reconstruction of flow field from real sparse data by a physics-oriented...

Physics perception in sloshing scenes with guaranteed thermodynamic consistency

Physics perception very often faces the problem that only limited data o...