Scientific Computing with Python

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Related lectures (29)

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Explores efficient stochastic numerical methods for modeling and learning, covering topics like the Analytical Engine and kinase inhibitors.

Mechanics of Slender Structure: Naturally Curly Hair in 3D

Delves into the mechanics of slender structures using naturally curly hair in 3D as an example.

Numerical Methods: Bisection and Multidimensional Arrays

Discusses the bisection method for solving nonlinear equations and its implementation using Python with NumPy and Matplotlib.

Numerical Methods: Stopping Criteria, SciPy, and Matplotlib

Discusses numerical methods, focusing on stopping criteria, SciPy for optimization, and data visualization with Matplotlib.

Numerical Analysis: Introduction to Interpolation Techniques

Covers the basics of numerical analysis, focusing on interpolation methods and their applications in engineering.

Verification and validation

Covers the verification and validation process in numerical flow simulation, ensuring credibility of simulation outcomes.

Numerical Integration: Introduction to SciPy and Matplotlib

Covers numerical integration techniques using SciPy and Matplotlib for visualizing functions and approximating integrals.

Vectorization in Python: Efficient Computation with Numpy

Covers vectorization in Python using Numpy for efficient scientific computing, emphasizing the benefits of avoiding for loops and demonstrating practical applications.

Scientific Computing in Neuroscience

Explores scientific computing in neuroscience, emphasizing the simulation of neurons and networks using tools like NEURON, NEST, and BRIAN.

Biological Scope and Computational Simulation

Explores connecting biological scope with computational simulation methods and discusses various simulation environments for modeling neural activity.