Scientific Computing with Python

Related lectures (29)

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Covers the application of numerical methods to solve differential equations using MATLAB.

Covers root-finding methods, focusing on the bisection and secant techniques, their implementations, and comparisons of their convergence rates.

Discusses finite differences and finite elements, focusing on variational formulation and numerical methods in engineering applications.

Covers Ordinary Differential Equations, first-order solutions, and numerical methods for IVP and BVP.

Explores trade-offs between data and time in computational problems, emphasizing diminishing returns and continuous trade-offs.

Explores implicit schemes in numerical analysis, emphasizing stability and convergence properties in solving differential equations.

Covers the basics of computational geomechanics, including poroelasticity, plasticity, and numerical methods for solving geotechnical problems.

Explores Monte Carlo simulation of 2D spin systems and the Ising model.

Introduces Pandas, Numpy, and Matplotlib for data analysis and visualization in Python.