Lectures related to Introduction to Numerical Methods in Scientific Computing

Covers the basics of scientific computing with Python, focusing on programming, algorithms, and numerical methods.

Explores the history and tools of scientific computing in neuroscience, emphasizing the simulation of neurons and networks.

Introduction to NumPy: Basics of Scientific Computing

Introduces NumPy, focusing on array creation, manipulation, and its advantages for scientific computing.

Numerical Methods: Stopping Criteria, SciPy, and Matplotlib

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

Data Science Essentials: Pandas, Numpy, Matplotlib

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

NumPy Arrays and Graphical Representations: Introduction

Covers NumPy arrays and their graphical representations using Matplotlib, focusing on array creation, manipulation, and visualization techniques.

Computational Science and Engineering: Master Program Overview

Outlines the Master in Computational Science and Engineering program at EPFL, detailing its structure, projects, and career opportunities for graduates.

Verlet Scheme: Magnetic Fields and Speed Dependent Forces

Explores the Verlet scheme, magnetic fields, speed-dependent forces, and nonlinear physics through simulations and exercises.

Numerical Methods: Bisection and Multidimensional Arrays

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

Scientific Computing Essentials

Covers algorithmic thinking, Python programming, numerical methods, and essential computing concepts for scientific computing.

Efficient Stochastic Numerical Methods

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

Jupyter Notebooks for Numerical Analysis

Covers the transition to Python and Jupyter notebooks for Numerical Analysis at EPFL, including interactive exercises and graded tests.

Numerical Methods: Differential Equations

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

Numerical Analysis: Introduction to Computational Methods

Covers the basics of numerical analysis and computational methods using Python, focusing on algorithms and practical applications in mathematics.

Numerical Methods in Biomechanics: Introduction

Introduces the importance of numerical methods in biomechanics and their application in analyzing observations, testing hypotheses, and improving medical devices and treatments.

Numerical Analysis: Introduction to Interpolation Techniques

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

Nonlinear Equation Resolution: Introduction to Bisection Method

Introduces the bisection method for resolving nonlinear equations using numerical techniques and Python programming.

Numerical Integration: Introduction to SciPy and Matplotlib

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

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.

Richards Equation: Water Transfers Simulation

Explores water transfers simulation using the Richards equation and numerical methods.