SUMMARY
A second-order tensor is defined as a multi-linear map from two vectors in a vector space V and its dual V* to a scalar, which can also be represented as a two-dimensional matrix. The rank of the tensor is the sum of the number of vectors it maps, with a second-order tensor having a rank of 2. The discussion emphasizes that while matrices can represent tensors, they are not equivalent to the tensor itself. Various definitions exist, but they are mathematically equivalent, and formal definitions are best found in textbooks rather than online sources.
PREREQUISITES
- Understanding of vector spaces and dual spaces
- Familiarity with multi-linear maps
- Knowledge of tensor ranks and orders
- Basic concepts of general relativity and Einstein's equations
NEXT STEPS
- Study the mathematical definition of tensors in "Introduction to Tensor Analysis and the Calculus of Moving Surfaces" by G. Strang
- Explore the concept of multi-linear maps in linear algebra
- Review the role of tensors in general relativity, particularly the Einstein tensor
- Examine the differences between tensors and matrices in "Linear Algebra Done Right" by Sheldon Axler
USEFUL FOR
Mathematicians, physicists, and engineering professionals seeking a rigorous understanding of tensors, particularly in the context of general relativity and advanced mathematics.