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A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

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Modeling of dynamical systems through deep learning.

P Rajendra¹, V Brahmajirao²

¹Department of Mathematics CMR Institute of Technology, Bengaluru, India. rajendra.padidhapu@gmail.com.

Biophysical Reviews

|November 22, 2020

Summary

This summary is machine-generated.

This review explores modern dynamical systems, focusing on discovering dynamics from data and simplifying nonlinear systems. Machine learning offers powerful solutions for these challenges, advancing data-driven modeling and analysis.

Keywords:

Deep learning Dimensionality reduction Dynamic mode decomposition Dynamical systems Machine learning

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Area of Science:

Dynamical Systems
Data Science
Machine Learning

Background:

Modern dynamical systems face challenges in discovering dynamics from data.
Making nonlinear systems amenable to linear analysis is a key goal.
Understanding unknown or partially known dynamics is crucial.

Purpose of the Study:

To present a modern perspective on dynamical systems.
To highlight challenges in data-driven discovery and representation.
To explore machine learning techniques for addressing these challenges.

Main Methods:

Reviewing emerging techniques in data science and machine learning.
Utilizing dimensionality reduction for projecting dynamical methods.
Applying deep learning techniques for inferring physical systems.

Main Results:

Machine learning provides powerful algorithms for nonlinear dynamics.
Data-driven models can discover governing equations and physical laws.
Deep learning methods like autoencoders and recurrent neural networks are effective.

Conclusions:

Machine learning is pivotal for tackling nonlinear and unknown dynamics.
Advanced deep learning methods enhance the modeling of dynamical systems.
This review offers insights into current goals and open challenges in the field.