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Comprehensive exploration of Singular Value Decomposition, covering mathematical foundations, applications in data processing, and implementation in MATLAB and Python for various real-world scenarios.
Explore Kalman Filter implementation on inverted pendulum using Matlab, covering theory and practical application in dynamical systems and control engineering.
Explore machine learning applications in fluid dynamics, from turbulence modeling to control strategies, with a focus on data-driven approaches and advanced computational techniques.
Explora técnicas avanzadas de aprendizaje automático, sistemas dinámicos y control con ejemplos prácticos en Matlab y Python, basados en el libro "Data-Driven Science and Engineering" de Brunton y Kutz.
Comprehensive review of Taylor and Power Series with visual aids and coding examples in Python and Matlab. Covers definitions, expansions for sine and cosine, and practical implementations.
Explore the complex logarithm, its infinite values, branch cuts, and principle n-th roots. Gain insights into this inverse of the complex exponential and its unique properties.
Introduction to complex numbers, their arithmetic operations, Euler's formula, and polar coordinates. Covers solutions to differential equations and lays foundation for further complex analysis topics.
Explore dynamical systems: fixed points, linearization, manifolds, bifurcations, and chaos. Gain insights into nonlinear dynamics and their applications in describing our changing world.
Explore solving the wave equation using Laplace transforms, covering problem setup, PDE to ODE conversion, and solution methods including the Heaviside function and method of characteristics.
Explore Moore's Law, exponential growth in computing, and its impact on AI, scientific computing, and future technologies. Understand the implications for technological advancement.
Explore techniques to analyze nonlinear differential equations by linearizing dynamics near fixed points, enabling powerful linear ODE solutions for nonlinear systems.
Learn to solve systems of linear ODEs using eigenvalues and eigenvectors. Covers problem setup, solution methods, and intuitive interpretation, with code examples in Python and MATLAB.
Explore eigenvalues and eigenvectors through differential equations, deriving key concepts and visualizing their applications in matrix systems for a deeper understanding of linear algebra.
Learn to transform high-order linear differential equations into matrix systems, enabling powerful solutions using eigenvalues and eigenvectors. Explore practical examples and key concepts in linear algebra applications.
Learn to solve high-order linear differential equations using characteristic polynomials and linear superposition. Covers guessing solutions, finding general solutions, and applying initial conditions.
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