Fast and simple nonlinear solvers for the SciML common interface. Newton, Broyden, Bisection, Falsi, and more rootfinders on a standard interface.

Automata on arbitrary networks, with Python

Parallel Data Assimilation Framework

Non-Linear Dynamic Systems

In this project, an observer in the form of a stable neural network is proposed for any nonlinear MIMO system. As a result of experience, this observer utilizes a nonlinear in parameter neural network (NLPNN) which unlike LPNN, supports systems with higher degree of nonlinearity with no pre-knowledge of its dynamics. The learning rule for this n…

Obtaining the best coefficients of Inverse Dynamics Controller, for a dynamical system, with Optimization Algorithms.

This repository includes different versions of the prescribed-time controller as Simulink blocks and MATLAB script codes for engineering applications.

Computing Irreversible Evolutions

ODESCA is a MATLAB tool for the creation and analysis of dynamic systems described by ordinary differential equations

Model-based Calibration of Multiple Injections for a CI engine

ForSolver - linear and nonlinear solvers

This repository includes some examples for the suboptimal active disturbance rejection controller (S-ADRC). The files are written in MATLAB and Simulink.

Reproducible code for our paper, "On Causal Discovery with Convergent Cross Mapping"

C++11 implementation of numerical algorithms described in Numerical Analysis by Richard L. Burden and J. Douglas Faires

MATLAB toolbox for analysing controllability and accessibility of nonlinear systems.

Newton's method for solving systems of nonlinear equations using the Faer library.

In this project a rather brilliant observer called Thau observer or Lipschitz observer is proposed and designed to estimate the states of a special form of nonlinear systems. All the details regarding the observer design and its simulation are given in "Kian Khaneghahi - Fault Midterm - Q4.pdf" report file.

Repository for my Nonlinear Dynamics files

A novel neural network for effective learning of highly impulsive/oscillatory dynamic systems by jointly utilizing low-order derivatives

Kalman filtering is a powerful technique for estimating the state of nonlinear mechatronics systems from noisy measurements. Kalman filters have a wide range of applications in robotics, vehicle control, and aircraft control.