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The inner ear assumes dual functionalities of auditory perception and equilibrium maintenance. The vestibule is the organ responsible for balance. This organ contains mechanoreceptors, specifically hair cells, endowed with stereocilia, which aid in deciphering information regarding the position and motion of our heads. Two intrinsic components, the utricle and saccule, help perceive head position, while the semicircular canals track head movement. Neurological messages initiated in the...

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Related Experiment Video

Updated: Nov 17, 2025

A Modified Lean and Release Technique to Emphasize Response Inhibition and Action Selection in Reactive Balance

A Modified Lean and Release Technique to Emphasize Response Inhibition and Action Selection in Reactive Balance

Published on: March 19, 2020

Learning dominant physical processes with data-driven balance models.

Jared L Callaham¹, James V Koch², Bingni W Brunton³

¹Department of Mechanical Engineering, University of Washington, Seattle, WA, USA. jc244@uw.edu.

Nature Communications

|February 16, 2021

Summary

This summary is machine-generated.

This study introduces a data-driven approach to automatically identify dominant physical processes in complex systems. This method generalizes traditional physics-based modeling to non-asymptotic regimes, uncovering key mechanistic models across diverse scientific fields.

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Area of Science:

Physics-based modeling
Data-driven science
Computational science

Background:

Traditional physics-based modeling relies on approximations of dominant processes.
This approach is mathematically complex and limited to asymptotic regimes with scale separation.
A need exists for generalized methods applicable to non-asymptotic regimes.

Purpose of the Study:

To automate and generalize the identification of dominant physical balances.
To develop data-driven balance models for non-asymptotic regimes.
To uncover key mechanistic models in complex systems.

Main Methods:

Introduction of an 'equation space' concept.
Utilizing unsupervised learning to identify subspace clusters representing local balances.
Applying data-driven models to identify negligible terms in equations.

Main Results:

Successfully delineated dominant balance physics in a wider range of systems.
Demonstrated the efficacy of data-driven balance models.
Uncovered key mechanistic models in turbulence, combustion, nonlinear optics, geophysical fluids, and neuroscience.

Conclusions:

The proposed data-driven approach effectively automates and generalizes physics-based modeling.
This method overcomes limitations of traditional asymptotic approaches.
It offers a powerful tool for discovering fundamental physics in complex scientific domains.