Laplace Transform , Inverse Transform, for differentiation & Integration

Numerical analysis with Python

This Python library is designed to facilitate the calculation of numerical derivatives of mathematical functions by employing advanced finite difference methods.

This is a technical document about physics, explaining fundamental kinematics concepts with calculus-based algebraic derivations. Occasional Python scripts are included for verification and justification of assumptions and derivations made in the explanatory sections.

This repository contains a Python implementation of numerical differentiation using Forward, Backward, and Centered methods with nth order derivatives. The code allows users to select the differentiation point, step size, method of differentiation, and the accuracy of the result.

Basic python module for beginners demonstrating different numerical techniques

Variational Inference with Numerical Derivatives: variance reduction through coupling

👾 numerical methods for physics from grad school at stony brook

A Symbolic Differentiator

A very small (<90 LOC) pure python library capable of computing gradients of basic arithmetic functions

Low-effort Automatic Zeroing Your-workload Differentiation, an auto-diff engine for effortless backpropagation in neural networks.

https://github.com/Jonas-Nicodemus/PINNs-based-MPC

Direct Warper implementation used in my PhD thesis (2019)

Python library for calculating formulas for derivatives.

A symbolic integration calculator built from scratch (no CAS libraries for the core). UI and secondary utilities may use libraries, but the integration engine, parser, AST, simplifier, differentiation, and rule system are handwritten.

A simple framework for doing automatical differentiation

Numerical Analysis algorithms in Python and Octave

A simple web app that calculates the equation of a tangent line of a function at a given point.

A package that allows developers to evaluate and differentiate functions, similar to SymPy or Maple.