lorenz-system

Implementation and study of the Lorenz 1980 model in Python 🌪️

Lorenz Attractor

Plain Convolution Encryption as an Alternative to Overcoming the Limitations of Synchronization-Based Methods

🌀 Illustration of the Lorenz System

Lorenz system - Repository for the project for Software and Computing for Applied Physics

Feature-based analysis and classification of nonlinear time-series dynamics using statistical, spectral, entropy, autocorrelation, DFA, PCA, UMAP, clustering, and cross-validated machine learning, with Lorenz dynamics as a reference.

Computation of Unstable Periodic Orbits for the Lorenz system

This repository contains the code for the blog post on Solving the Lorenz system using Runge-Kutta methods. For further details, please refer to this post.

Hybrid Physics-AI for forecasting the Lorenz-63 chaotic system with Explainable AI (gradient saliency). Compares Physics Baseline, MLP, and Hybrid (RK4 + residual NN) on short-term MSE, divergence time, and attractor similarity.

Code for the paper "System Identification with Copula Entropy"

Python project evaluating multi-process parallelism for numerical simulation of the Lorenz system using RK4 and multiprocessing

A high-fidelity Physics-Informed Neural Network (PINN) research platform for solving the 1D convection-diffusion equation with enforced conservation of mass. Neural ODE · PINN · NODE-ONet · Real-time inference · Interactive dashboard.

Neural networks for structured data classification and multi-horizon time series forecasting; heart disease risk, weather, Lorenz chaos, and tumor growth. Deep Learning Research Internship, Universität Koblenz.

A Python framework for chaotic/hyperchaotic synchronization, control, disturbance analysis, and nonlinear dynamical systems simulation.

This project features two dynamic simulations: bungee jumping and atmospheric convection models, using Runge-Kutta methods to capture their behavior. Dive into chaotic Lorenz attractor visuals, track variable evolution via time series charts, and compare cord lengths between these intriguing simulations. Explore dynamic modeling and chaotic systems

MATLAB benchmark suite for comparing numerical integrators on classical differential-equation problems, including linear decay, harmonic oscillator, Van der Pol, Lorenz, Robertson kinetics, and Kepler two-body dynamics. The repository includes performance plots, accuracy metrics, conservation analysis, and a simulation video available on YouTube.

Can an echo state network rebuild the Lorenz bifurcation diagram at chaos parameters it never saw in training? This is the code and running log for that experiment.

Decoupled autoencoder framework: bounded + unbounded latent carriers with two-stage frozen training. Lorenz-63 chaotic system benchmark.

Numerical solution of the Lorenz system using the classical fourth-order Runge–Kutta method, including phase-space and 3D attractor visualization.