Lectures related to Richardson extrapolation

Numerical Differentiation: Richardson Extrapolation

Covers Richardson extrapolation for numerical differentiation to reduce error.

Introduces Simpson's rule for numerical integration and Richardson extrapolation for accuracy improvement.

Computation, Verification and Validation

Explains verification and validation in CFD simulations, focusing on Richardson extrapolation and accuracy checking.

Error Estimation and Numerical Integration

Explores error estimation in numerical integration and its applications in forecasting, emphasizing the Romberg method and Richardson extrapolation.

Universal Approximation Theorem: MLP

Covers Multi-Layer Perceptrons (MLP) and their application from classification to regression, including the Universal Approximation Theorem and challenges with gradients.

Splines: Fundamentals and Applications

Explores B-splines, natural cubic splines, and smoothing splines in regression problems and their practical applications.

Double Descent Curves: Overparametrization

Explores double descent curves and overparametrization in machine learning models, highlighting the risks and benefits.

Numerical Integration: Lagrange Interpolation, Simpson Rules

Explains Lagrange interpolation for numerical integration and introduces Simpson's rules.

Iterative Methods: Error Control and Linear Systems Resolution

Explores iterative methods for solving linear systems with a focus on error control.

Rare Events and Extreme Modeling

Explores rare event modeling, challenges in estimating probabilities with limited data, and the application of extreme value theory in various fields.

Determinantal Point Processes and Extrapolation

Covers determinantal point processes, sine-process, and their extrapolation in different spaces.

Calculating Average Growth Rates

Explores the calculation of average growth rates and the sensitivity of growth rates to different time periods.

Linear Systems: Iterative Methods

Covers iterative methods for solving linear systems, including Jacobi and Gauss-Seidel methods.

Numerical Analysis: Linear Systems

Covers the analysis of linear systems, focusing on methods such as Jacobi and Richardson for solving linear equations.

Verification and validation

Covers the verification and validation process in numerical flow simulation, ensuring credibility of simulation outcomes.

Iterative Methods: Linear Systems

Covers iterative methods for solving linear systems and discusses convergence criteria and spectral radius.