Search results for Control systems and optimization

11 - Reduced Order Models (ROMs)
from Part IV - Reduced Order Models
Steven L. Brunton, University of Washington, J. Nathan Kutz, University of Washington
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Data-Driven Science and Engineering

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15 February 2019

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28 February 2019, pp 375-402
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Glossary
Steven L. Brunton, University of Washington, J. Nathan Kutz, University of Washington
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Data-Driven Science and Engineering

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15 February 2019

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28 February 2019, pp 436-442
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12 - Interpolation for Parametric ROMs
from Part IV - Reduced Order Models
Steven L. Brunton, University of Washington, J. Nathan Kutz, University of Washington
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Data-Driven Science and Engineering

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15 February 2019

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28 February 2019, pp 403-435
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Part III - Dynamics and Control
Steven L. Brunton, University of Washington, J. Nathan Kutz, University of Washington
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Data-Driven Science and Engineering

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15 February 2019

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28 February 2019, pp 227-228
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Index
Steven L. Brunton, University of Washington, J. Nathan Kutz, University of Washington
Book:

Data-Driven Science and Engineering

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15 February 2019

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28 February 2019, pp 471-472
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3 - Sparsity and Compressed Sensing
from Part I - Dimensionality Reduction and Transforms
Steven L. Brunton, University of Washington, J. Nathan Kutz, University of Washington
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Data-Driven Science and Engineering

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15 February 2019

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28 February 2019, pp 84-114
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7 - Data-Driven Dynamical Systems
from Part III - Dynamics and Control
Steven L. Brunton, University of Washington, J. Nathan Kutz, University of Washington
Book:

Data-Driven Science and Engineering

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15 February 2019

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28 February 2019, pp 229-275
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10 - Data-Driven Control
from Part III - Dynamics and Control
Steven L. Brunton, University of Washington, J. Nathan Kutz, University of Washington
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Data-Driven Science and Engineering

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15 February 2019

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28 February 2019, pp 345-372
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Bibliography
Steven L. Brunton, University of Washington, J. Nathan Kutz, University of Washington
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Data-Driven Science and Engineering

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15 February 2019

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28 February 2019, pp 443-470
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Preface
Steven L. Brunton, University of Washington, J. Nathan Kutz, University of Washington
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Data-Driven Science and Engineering

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15 February 2019

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28 February 2019, pp ix-xii
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9 - Balanced Models for Control
from Part III - Dynamics and Control
Steven L. Brunton, University of Washington, J. Nathan Kutz, University of Washington
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Data-Driven Science and Engineering

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15 February 2019

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28 February 2019, pp 321-344
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2 - Fourier and Wavelet Transforms
from Part I - Dimensionality Reduction and Transforms
Steven L. Brunton, University of Washington, J. Nathan Kutz, University of Washington
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Data-Driven Science and Engineering

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15 February 2019

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28 February 2019, pp 47-83
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6 - Neural Networks and Deep Learning
from Part II - Machine Learning and Data Analysis
Steven L. Brunton, University of Washington, J. Nathan Kutz, University of Washington
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Data-Driven Science and Engineering

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15 February 2019

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28 February 2019, pp 195-226
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Part II - Machine Learning and Data Analysis
Steven L. Brunton, University of Washington, J. Nathan Kutz, University of Washington
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Data-Driven Science and Engineering

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15 February 2019

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28 February 2019, pp 115-116
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8 - Linear Control Theory
from Part III - Dynamics and Control
Steven L. Brunton, University of Washington, J. Nathan Kutz, University of Washington
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Data-Driven Science and Engineering

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15 February 2019

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28 February 2019, pp 276-320
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1 - Singular Value Decomposition (SVD)
from Part I - Dimensionality Reduction and Transforms
Steven L. Brunton, University of Washington, J. Nathan Kutz, University of Washington
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Data-Driven Science and Engineering

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15 February 2019

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28 February 2019, pp 3-46
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Contents
Steven L. Brunton, University of Washington, J. Nathan Kutz, University of Washington
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Data-Driven Science and Engineering

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15 February 2019

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28 February 2019, pp v-viii
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Part I - Dimensionality Reduction and Transforms
Steven L. Brunton, University of Washington, J. Nathan Kutz, University of Washington
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Data-Driven Science and Engineering

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15 February 2019

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28 February 2019, pp 1-2
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Common Optimization Techniques, Equations, Symbols, and Acronyms
Steven L. Brunton, University of Washington, J. Nathan Kutz, University of Washington
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Data-Driven Science and Engineering

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15 February 2019

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28 February 2019, pp xiii-xxii
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Data-Driven Science and Engineering

Machine Learning, Dynamical Systems, and Control
Steven L. Brunton, J. Nathan Kutz
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15 February 2019

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28 February 2019
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Data-driven discovery is revolutionizing the modeling, prediction, and control of complex systems. This textbook brings together machine learning, engineering mathematics, and mathematical physics to integrate modeling and control of dynamical systems with modern methods in data science. It highlights many of the recent advances in scientific computing that enable data-driven methods to be applied to a diverse range of complex systems, such as turbulence, the brain, climate, epidemiology, finance, robotics, and autonomy. Aimed at advanced undergraduate and beginning graduate students in the engineering and physical sciences, the text presents a range of topics and methods from introductory to state of the art.

Control systems and optimization

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1550 results in Control systems and optimization

11 - Reduced Order Models (ROMs)

Glossary

12 - Interpolation for Parametric ROMs

Part III - Dynamics and Control

Index

3 - Sparsity and Compressed Sensing

7 - Data-Driven Dynamical Systems

10 - Data-Driven Control

Bibliography

Preface

9 - Balanced Models for Control

2 - Fourier and Wavelet Transforms

6 - Neural Networks and Deep Learning

Part II - Machine Learning and Data Analysis

8 - Linear Control Theory

1 - Singular Value Decomposition (SVD)

Contents

Part I - Dimensionality Reduction and Transforms

Common Optimization Techniques, Equations, Symbols, and Acronyms

Data-Driven Science and Engineering

Control systems and optimization

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1550 results in Control systems and optimization

Data-Driven Science and Engineering