Using the dynamic mode decomposition to forcast sales from the m5 competition
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Updated
Jan 9, 2024 - Python
Using the dynamic mode decomposition to forcast sales from the m5 competition
For research into the application of Koopman operators at Boston University.
This research work is about Limited Data Acquisition for the real life physical experiment of fluid flow across cylinder based on Kernelized Extended Dynamic Mode Decomposition by incorporating Gaussian Random Matrix Theory and Laplacian Kernel Function Hilbert space.
Constructing linearizing transformations for reduced-order modeling of nonlinear dynamical systems
Non-intrusive Reduced Order Modeling package
Implementation of Online DMD using NumPy
a little library to help me with things involving Koopman operators
A Python Implementation of Dynamic Mode Decomposition
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.
Dynamic Mode Decomposition (DMD)
Python tools for non-intrusive reduced order modeling
AutoKoopman - automated Koopman operator methods for data-driven dynamical systems analysis and control.
Tensor Train Toolbox
Modred main repository
flowTorch - a Python library for analysis and reduced-order modeling of fluid flows
Python Dynamic Mode Decomposition
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