Lecture

Partial Differential Equations: Reflow and Light Propagation

Related lectures (29)

Covers mathematical concepts from number theory to probability and statistics.

Delves into quantum mechanics, focusing on probability in potential wells and comparing classical and quantum predictions.

Covers fundamental concepts of probability and statistics, focusing on data analysis, graphical representation, and practical applications.

Explores the strong convergence of random variables and the normal distribution approximation in probability and statistics.

Covers fundamental concepts in probability and statistics, emphasizing data analysis techniques and statistical modeling.

Introduces key concepts in probability and statistics, such as events, Venn diagrams, and conditional probability.

Explores joint random variables, conditional density, and independence in probability and statistics.

Explores continuous random variables, density functions, joint variables, independence, and conditional densities.

Explores conditional density, expectations, and independence of random variables with practical examples.

Explores variance, covariance, and practical applications in statistics and probability.

Explores dependence, correlation, and conditional expectations in probability and statistics, highlighting their significance and limitations.

Introduces key concepts in probability and statistics, covering random experiments, events, intersections, unions, and more.

Explores the Central Limit Theorem, covariance, correlation, joint random variables, quantiles, and the law of large numbers.

Covers the solution of Maxwell equations using advanced and retarded Green functions, focusing on electrostatics with general boundary conditions.

Explores conditional probability, Bayes' theorem, and prediction decomposition for informed decision-making.

Covers analytical solutions for neutron diffusion from planar sources in different media.

Covers Simpson's paradox, probability distributions, and real-life examples in probability and statistics.

Introduces key concepts in probability and random variables, covering statistics, distributions, and covariance.

Explores fundamental theorems in probability and statistics, joint probability laws, and marginal distributions.