/gradient_ML_LTI

A gentle introduction to custom gradient propagation for ML application in which parameters of LTI systems have to be optimized. This example enables the integration of control theory with machine learning, for the development of Physical-Informed Neural Networks (PINNs)

Primary LanguageJupyter NotebookMIT LicenseMIT

Gradient propagation in LTI systems for machine learning applications

A gentle introduction to custom gradient propagation for ML application in which parameters of LTI systems have to be optimized. This example enables the integration of control theory with machine learning, for the development of Physical-Informed Neural Networks (PINNs).

Verify to have the needed packages for this example. Alternatively, you can install them via

pip install -r requirements.txt

Please refer to this Jupyter Notebook for the illustration of the workflow.