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Computational Quantum Physics
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Related lectures (30)
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Quantum Physics I
Explores eigenvalues, ladder operations, and quantum number values in quantum physics.
Explicit Stabilised Methods: Applications to Bayesian Inverse Problems
Explores explicit stabilised Runge-Kutta methods and their application to Bayesian inverse problems, covering optimization, sampling, and numerical experiments.
Discrete Symmetries: Asymptotic States and S-matrix
Covers the concept of discrete symmetries, focusing on the introduction to asymptotic states and S-matrix.
Matrix Eigenvalues and Eigenvectors
Covers matrix eigenvalues, eigenvectors, and their linear independence.
Cyclic Subspace Decomposition
Explores minimal annihilating polynomials and cyclic invariant subspaces, showcasing their practical applications through matrix computations.
Linear Algebra: Matrices and Linear Applications
Covers matrices, linear applications, vector spaces, and bijective functions.
Numerical Analysis: Advanced Topics
Covers advanced topics in numerical analysis, focusing on techniques for solving complex mathematical problems.
Computational Quantum Physics
Covers variational methods and time-dependent principles in computational quantum physics.
Scaling & Renormalization in Statistical Mechanics
Explores scaling and renormalization in statistical mechanics, emphasizing critical points and invariant properties.
Eigenvalues and Optimization: Numerical Analysis Techniques
Discusses eigenvalues, their calculation methods, and their applications in optimization and numerical analysis.
