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Related lectures (29)
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Diagonalization of Symmetric Matrices
Covers the diagonalization of symmetric matrices, the spectral theorem, and the use of spectral decomposition.
Partial Differentiation
Explores simple examples of partial differentiation, demonstrating how to calculate partial derivatives and gradients in various scenarios.
Stochastic Numerical Methods for Quantum Systems
Explores exact diagonalization, mean field states, and Metropolis-Hastings algorithm for quantum systems.
Power Method
Covers the Power Method for approximating the largest eigenvalue of a matrix.
Symmetric Matrices and Diagonalization
Explores symmetric matrices, diagonalization, and their real-world applications in data analysis.
Second Fundamental Tensor
Explores the second fundamental tensor and its role in calculating normal curvature on surfaces.
Matrix Decomposition: Triangular and Spectral
Covers the decomposition of matrices into triangular blocks and spectral decomposition.
Linear Statics of Deformable Solids
Introduces linear statics for linear elastic solids in small deformations, stress equilibrium, the Virtual Work Principle, and the Finite Element Method.
Linear Applications: Vector Spaces and Subspaces
Explores linear applications in vector spaces, emphasizing subspaces and properties of linear maps.
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