Vector Fields: Gradient and Divergence

Related lectures (25)

Page 2 of 3

Explores the optimization of neural networks using Stochastic Gradient Descent (SGD) and the concept of dual risk versus empirical risk.

Covers vector calculus concepts like gradient, divergence, and curl with applications in physics and engineering.

Explores how sources and sinks affect the divergence of vector fields.

Covers the analysis of scalar fields, including divergence, gradient, and Laplacian.

Covers surface integrals, the divergence theorem, and Stocks' theorem through examples and analogies.

Explores the application of the Gauss/Green theorem to calculate curve integrals along simple closed curves.

Explores surface integrals using parameterization and the divergence theorem, with practical examples included.

Covers unit vectors, coordinate system conversion, divergence, and rotational concepts in vector analysis.

Explores gradient, divergence, and integral theorems in vector calculus.

Covers the concept of divergence in vector calculus and its computation with examples.