Numerical simulations play a pivotal role in understanding complex systems governed by differential equations. Python, with its extensive libraries like SciPy, NumPy, and Matplotlib, provides a robust environment for simulating and analyzing ordinary and partial differential equations. This guide covers the essentials of setting up and conducting numerical simulations for ODEs and PDEs using Python.

Differential equations are at the heart of many engineering, physics, and mathematics problems. Python’s SciPy library offers powerful tools to solve these equations. This guide will walk you through solving differential equations using SciPy, covering both ordinary and partial differential equations.

In this blog post, I will show you how to use scipy.optimize.least_squares to solve nonlinear least squares problems in Python. Nonlinear least squares problems are optimization problems where the objective function is a sum of squared residuals, and the residuals depend on some nonlinear function of the parameters. For example, suppose we have some data Continue reading

In this blog post, I will show you how to use scipy curve_fit to fit a function with multiple independent variables. Curve fitting is a technique to find the best parameters for a model function that describes the relationship between a dependent variable and one or more independent variables. Scipy curve_fit is a function in Continue reading

You will learn how to calculate the factorial of an array in Numpy.

Calculating definite integrals is a fundamental operation in calculus and numerical analysis. Python, with its powerful libraries, provides an efficient way to calculate these integrals, particularly using the SciPy library.

Let’s check how to perform Chi-Squared test using SciPy Python library.

Creating a normal distribution plot is a common task in statistics and data analysis. Let’s see how to generate distribution plot the easiest way in Python.

Let’s see how to calculate the geometric mean in Python. The geometric mean is a measure of central tendency that is useful for dealing with data that has a wide range of values and is often used in finance, biology, and other fields.