There are 2 repositories under dynamic-mode-decomposition topic.
flowTorch - a Python library for analysis and reduced-order modeling of fluid flows
Scripts and notebooks to accompany the book Data-Driven Methods for Dynamic Systems
This repository contains lecture notes and codes for the course "Computational Methods for Data Science"
AutoKoopman - automated Koopman operator methods for data-driven dynamical systems analysis and control.
Dynamic Mode Decomposition (DMD)
This repository contains all the work developed in the context of the Master Thesis dissertation entitled Model Predictive Control for Wake Steering: a Koopman Dynamic Mode Decomposition Approach. The repository includes all developed documentation (dissertation, extended abstract, poster and presentation) source code (MATLAB script and functions), datasets and results (animations, articles).
Extended Dynamic Mode Decomposition for system identification from time series data (with dictionary learning, control and streaming options). Diffusion Maps to extract geometric description from data.
a little library to help me with things involving Koopman operators
Implementation of Online DMD using NumPy
Neural Ordinary Differential Equations for model order reduction of time-dependent PDEs
Dynamic Mode Decomposition (DMD)
A short introduction and implementation of the Dynamic Mode Decomposition with applications.
An Incremental Approach to Online Dynamic Mode Decomposition for Time-Varying Systems with Applications to EEG Data Modeling
A Python Implementation of Dynamic Mode Decomposition
This repository is dedicated to the studienarbeit project of "Numerical investigation of 2D transonic shock-buffet around a NACA 0012-34 airfoil using OpenFOAM and flowTorch" undertaken by Mr. Tushar Gholap under the guidance of Dr. Andre Weiner.
Non-intrusive Reduced Order Modeling package
This repository stores my personal projects related to data science studies.
Code for the paper Bilinear Dynamic Mode Decomposition for Quantum Control
Contains code and detailed write ups created while taking a class on Data Driven Modeling & Scientific Computation with Professor Nathan Kutz.
Predict stocks using DMD, analyze & backtrack performance with Streamlit GUI. Learn ML for stock prediction & analysis with this GitHub repository
This research work is about Limited Data Acquisition for the real life physical experiment of fluid flow across cylinder based on Dynamic Mode Decomposition.
For research into the application of Koopman operators at Boston University.
Java Dynamic Mode Decomposition
Solvers to build tmodel approximation of DMD with incomplete information
Lane detection for autonomous car using Dynamic mode decomposition
Using the dynamic mode decomposition to forcast sales from the m5 competition