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Explore advanced techniques in dynamical systems analysis, including SINDy, Koopman theory, and deep learning applications for modeling complex nonlinear systems.
Explore modern machine learning methods for modeling complex dynamical systems from data. Learn data-driven control techniques and apply them to various fields of science and engineering.
Explora el Aprendizaje por Refuerzo, desde métodos básicos hasta redes neuronales y aplicaciones en dinámica de fluidos y control, en esta serie de 3.5 horas por Steve Brunton.
Explore sparsity and compression techniques, from compressed sensing to robust regression, with practical applications in image processing, signal analysis, and machine learning.
Comprehensive introduction to data science and machine learning, covering key concepts, techniques, and applications. Includes hands-on examples in MATLAB and Python for practical implementation.
Explore practical applications of complex integration theorems through challenging examples, enhancing your understanding of advanced mathematical concepts.
Explore the residue theorem and learn to compute complex contour integrals around singular points, culminating in the integral of f(z)=1/z.
Explore contour integration in complex analysis, covering theorems, continuous deformation, and integration around singularities for a deeper understanding of complex functions.
Explore how analytic complex functions' real and imaginary parts solve Laplace's equation, introducing harmonic functions in this in-depth mathematical analysis.
Explore analytic complex functions and learn to test for analyticity using Cauchy-Riemann conditions in this comprehensive mathematical exploration.
Explore complex logarithms and exponentials to compute fractional powers of complex numbers, including nth roots of unity and special cases like the square root of i.
Explore complex functions including monomials, exponentials, and trigonometric functions. Learn about Taylor series and important identities in complex analysis.
Explore chaos and uncertainty propagation in dynamical systems through numerical integration. Learn efficient vectorization techniques in Python and MATLAB for faster computations.
Learn to implement a 4th-order Runge-Kutta integrator in Python and Matlab, and apply it to simulate the chaotic Lorenz 1963 system. Gain practical coding skills for numerical integration.
Comprehensive introduction to Runge-Kutta schemes for numerical integration of differential equations, covering RK2 and RK4 methods with intuitive explanations and visual representations.
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