Scientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, R

This repository provides essential numerical algorithms for solving mathematical problems. Covering linear equations, differential equations and more, it's a valuable resource for students and professionals in science and engineering.

This repository is the first code made for my Master's Degree in Engineering, for the subject "Fundamentals of Engineering".

a gradient-based optimisation routine for highly parameterised non-linear dynamical models

MATLAB implementation of the Perona-Malik Diffusion (anisotropic diffusion). The algorithm is designed to denoise images while keeping edges intact. Both explicit and semi-implicit implementations can be found in the repository

Numerical methods implementation in MATLAB.

A MATLAB suite of initial value problems

Work in progress...

This repo contains implementations of several numerical methods in MATLAB.

Mathematical Transformation cloaking of Wave PDE

Large-scale implementation (using the Split-Apply-Combine paradigm) of different metabolic adaptation models of species in a 2-species co-culture, based on a Consumer-Resource modeling framework in a Serial-Dilution setup, where species compete for nutrients that are supplied periodically.

JUMPt Version 1.0.0

Mathematical modelling of microglial cells for the neuroscience community

A project which was made as a part of a university assignment and it includes different calculation of dynamical systems as well as finding stable points etc.

Implementations of various Algorithms used in Numerical Analysis, from root-finding up to gradient descent and numerically solving PDEs.

This repo contains the work from Group 3 in Math 485 from the University of Arizona and their prediction model for COVID-19 daily new cases.

Программная реализация методов Рунге-Кутты для решения задачи Коши

This project was done in partial fulfillment of the requirements for the module JEB 1444: Neural Engineering in Winter 2023. First, the FitzHugh-Nagumo model is used to explore the dynamics of a coupled system and generate input-output relationships. Secondly, Wiener Bose Model is used to learn a non-parametric model on input-output signals.

Numerical solution of Painlevé III D7 equation and calculation of canonical barriers according to arXiv:1806.06588

SIR approximation model