Scribbler articles on Scientific Computation

A list of libraries in JavaScript for performing statistical analysis in the browser or in the backend.

Scribbler offers a versatile, accessible, and cost-effective alternative to MATLAB. With its JavaScript environment, support for open-source libraries, built-in array utilities, and Plotly charting integration, Scribbler caters to a wide range of computational and data visualization needs.

JavaScript provides a versatile and powerful platform for performing statistical analysis, especially when combined with web technologies for data visualization and interactivity.

This article explores the diverse applications of JavaScript across various domains, including scientific computation, simulations, machine learning, fluid dynamics, equation solving, engineering design, data visualization, statistical analysis, bioinformatics, financial modeling, and robotics.

The Sugarscape model is a powerful tool for understanding complex social dynamics through simple rules. Implementing such a model in JavaScript allows for interactive and visual exploration of these dynamics.

Agent-based models (ABMs) have a wide range of applications across various fields. This article gives a outline of ABMs along with JavaScript code.

JavaScript, with its accessibility and powerful visualization capabilities, is a valuable tool for computational physics, especially in educational contexts and interactive web applications.

Solving differential equations with JavaScript opens up a world of possibilities for developers to tackle complex problems in science, engineering, and other fields. By leveraging numerical algorithms and libraries like numeric.js, developers can simulate dynamic systems, analyze trends, and make predictions based on mathematical models.

Curve fitting and interpolation techniques are powerful tools for analyzing and modeling data in JavaScript. Whether you're working with linear interpolation, polynomial interpolation, or cubic spline interpolation, there are libraries and methods available to suit your needs.

Explore the power of JavaScript and Plotly.js for data visualization in scientific research, showcasing interactive examples across climate science, genetics, healthcare, astronomy, and more.

Genetic algorithms are powerful optimization tools inspired by the principles of natural selection. This article includes detailed explanations of genetic algorithm concepts, step-by-step implementation using JavaScript, and practical examples to demonstrate how genetic algorithms can be used to solve complex optimization tasks.

Learn how to implement gradient descent for optimizing functions using JavaScript. This article provides a step-by-step guide, code examples, and an exercise to apply gradient descent in a JavaScript notebook environment for numerical optimization tasks.

JavaScript is a versatile language for various use-cases including scientific computation, AI, and machine learning. This article lists a few very useful libraries for scientific computation.

We look at how to use Monte Carlo simulation for solving various scientific and computation problems in JavaScript.

This comprehensive guide provides insights into the historical context, applications, advantages, limitations, and practical implementation of the Newton-Raphson method. Experiment with the provided JavaScript code and adapt it to solve specific equations or functions of interest.

In this article, we'll explore several numerical methods for integration and implement them using JavaScript. We will look at Reimann Sum, Trapezoidal Rule and Simpson's rule.

We bring the beauty of chaos and strange-attractors to the browser using an html5 canvas and JavaScript.

Using WebGL we generate visualization of audio in nice animation of Perlin Noise. We also show how GPU can be used from browser for high performance computation.

We will discuss three sorting algorithms using JavaScript - Bubble Sort; Quick Sort; and Merge Sort, for sorting arrays into an order.

Enumerating prime numers is very important in the fields of mathematics and computer science. There are several algorithms to do this and they can be easily implemented in JavaScript.

Spectral Analysis is a powerful tool used to solve scientific and engineering problems. JavaScript provides the toolkit to do this easily.

Numeric.js is a JavaScript library that provides a range of numerical computing and linear algebra/matrix computing functionalities.

Regula Falsi method, also known as the false position method, is a numerical method used to find the root of an equation within a given interval. It is one of the oldes and easily implmentable method.

Numerical analysis in JavaScript allows researchers, scientists, and engineers to tackle complex problems by formulating them as computational tasks and finding approximate solutions using numerical algorithms and techniques. JavaScript can be used to implement Bisection Method, Newton's Method, Euler's Method, Simpson's Rule, Gaussian Elimination and Runge-Kutta Method.

Monte-Carlo simulation can be used to price financial options and the code can be implemented in any programming language including Python and JavaScript.

Scientific simulation is an indispensable tool in modern science and engineering, enabling researchers to study and predict the behavior of complex systems in a virtual environment. JavaScript's blend of accessibility, interactivity, real-time performance, and extensive ecosystem makes it an ideal choice for developing simulations.