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Explore numerical integration techniques including Riemann integrals, trapezoidal rule, and Simpson's rule. Learn to approximate definite integrals with Python and MATLAB implementations.
Explore numerical computation of second derivatives and data differentiation with Python and Matlab examples, covering finite difference schemes and practical applications.
Learn techniques for approximating derivatives numerically using finite difference schemes. Explore error analysis with Taylor series and see practical implementations in MATLAB and Python.
Explores linear differential equations with forcing, covering impulse response, Dirac delta function, and convolution integral. Essential for control theory and linear systems analysis.
Explore diagonalization and Jordan Canonical Form for systems of differential equations, covering eigenvectors, complex conjugates, repeated eigenvalues, and general matrix applications.
Explore non-normal linear systems and their role in turbulence, including examples, code demonstrations, and applications in fluid dynamics and bypass transition.
Learn to analyze nonlinear systems by drawing phase portraits, identifying fixed points, linearizing, and inferring global dynamics. Gain practical skills in stability analysis using eigenvalues and eigenvectors.
Explore imaginary eigenvalues and center fixed points in 2D ODE systems. Learn to compute eigenvectors, write solutions, and understand geometric intuition behind rotational dynamics.
Explore unstable saddle points in 2D linear ODEs, their applications in energy-efficient transport, and analysis using eigenvalues, eigenvectors, and phase portraits.
Explore 2D linear ODEs with real eigenvalues, analyzing unstable sources and stable sinks through eigenvalues, eigenvectors, and phase portraits. Gain insights into solution methods and visualizations.
Explore eigenvalues and eigenvectors, key concepts in linear algebra. Learn to compute them for any matrix, understand their significance in transformations, and apply them to solve complex linear systems.
Explore advanced examples of second-order ODEs, including characteristic polynomials, initial conditions, and matrix systems. Learn to solve, plot, and interpret results using MATLAB.
High-level introduction to essential Python commands for engineering math, covering arithmetic, loops, arrays, plotting, and solving equations and differential equations.
High-level introduction to essential Matlab commands for engineering math, covering arithmetic, loops, matrices, plotting, and solving equations and differential equations.
Comprehensive tutorial on solving second-order ODEs for damped harmonic oscillators, covering derivation, solution methods, and practical implementation in Matlab and Python.
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