Introduction to Numerical Methods in Scientific Computing

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Biological Scope and Computational Simulation

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

Data Science with Python: Numpy Basics

Introduces the basics of Numpy, a numerical computing library in Python, covering advantages, memory layout, operations, and linear algebra functions.

Computational Geomechanics

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

ODEs: Introduction and Solutions

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

Programming for Engineers: MATLAB Integration and Differentiation

Explores MATLAB integration, differentiation, ODE solvers, and simulation techniques for dynamic systems.

Trade-offs in Data and Time

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

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.

Numerical Methods in Hydrodynamics

Covers numerical methods in hydrodynamics, ideal fluids, plasma universe, dark matter simulation, and baryon modeling.

Scientific Computing in Neuroscience

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

Verification and validation

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