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A framework for data-driven modeling and analysis of granular materials in the strongly nonlinear regime using the modern Koopman theory

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GM-Koopman: Data-driven Modeling and Analysis of Granular Materials with Modern Koopman Theory

This repository contains the source code for an ongoing project on data-driven modeling of granular materials using the modern Koopman theory.




Installation

Consider using a dedicated virtual environment (conda or otherwise) with Python 3.9+ and install the NeuroMANCER package:

pip install neuromancer

Usage

The code can run on CPU but using a CUDA-enabled GPU is recommended. DK_comp.py is the main program for running an experiment with a two-particle system. The particle configuration is passed as a command line argument so execute python DK_comp.py 0 to run an experiment for the first of the following four configurations:

Notes

This repository is part of an ongoing project and will be updated continuously.

Please do not hesitate to reach out directly or open a GitHub issue to start a conversation. Thank you for your interest in our work.

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A framework for data-driven modeling and analysis of granular materials in the strongly nonlinear regime using the modern Koopman theory

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