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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 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.
Explore machine learning techniques for data-driven control, covering system identification, model reduction, and advanced control strategies like MPC and reinforcement learning.
Explore Kalman Filter implementation on inverted pendulum using Matlab, covering theory and practical application in dynamical systems and control engineering.
Introduction to Physics Informed Neural Networks (PINNs): combining neural networks with physical laws to solve complex problems in fluid dynamics and beyond. Explores advantages, applications, and extensions of this innovative approach.
Explore quality control principles through multinomial distribution, learning to assess batch reliability using non-destructive inspection methods and statistical sampling techniques.
Analyze ordinary differential equations using eigenvalues and eigenvectors, laying the foundation for linear control theory in this comprehensive mathematical exploration.
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 conjugate priors through Normal distributions and the Exponential Family, demonstrating how Normal likelihoods pair with Normal priors for Bayesian inference.
Explore how to integrate physics into machine learning, enhancing model accuracy and efficiency in engineering applications. Learn to leverage prior physical knowledge across all stages of the ML process.
Explore exponential growth, its applications, and control theory in relation to COVID-19, covering models, sensors, and control design strategies.
Comprehensive exploration of Fourier analysis, covering series, transforms, and applications. Includes practical implementations in MATLAB and Python for signal processing, PDEs, and image compression.
Comprehensive exploration of linear algebra concepts, including SVD, Fourier analysis, and compressed sensing, with practical applications and coding examples in MATLAB and Python.
Explore advanced techniques in dynamical systems analysis, including Koopman theory, spectral analysis, and applications to control and chaos in various scientific domains.
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