A canonical problem in computer science is to find the shortest route to every point in a network. A new approach beats the classic algorithm taught in textbooks.

The proposed methods use a Chebyshev collocation technique as well as an asymptotical linear ordinary differential equation of first-order derived from the difference between the exact solution and ...

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Numerical simulations of motion in Python using Euler, Runge-Kutta, Leapfrog, and Adams-Bashforth methods. Simulates spiral, circular, harmonic, and rocket motion with visualizations and output ...

This repository contains a Python implementation for solving ordinary differential equations (ODEs) using various numerical methods, including the Euler method, Heun's method, the Midpoint method, and ...

A fast simulation method for time-derivative cellular neural networks (TDCNN) is proposed. Using forward Euler approximation (FEA) for the derivative of the cell state and the backward Euler ...

Different from directly modeling all the compartments and parameters using neural networks, leveraging the Euler iteration in forward process provides more constraints to the network. As a result, ...

Numerov’s numerical method is developed in a didactic way by using Python in its Jupyter Notebook version 6.0.3 for three different quantum physical systems: the hydrogen atom, a molecule governed by ...