GitHub - vasisthasinghal/Learning-Equations-of-a-Dynamical-System: The focus of function approximation problems has been on identifying some suitable function without attempting to gain insight into the mechanism of the system. The performance of the model boils down to interpolation. But, in a more realistic setting, we expect test data from outside the distribution of the training set. To better extrapolate to unseen domains, it is essential to learn the correct underlying equations of the system. The Equation Learner (EQL) Network attempts to achieve this task.

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Name		Name	Last commit message	Last commit date
Latest commit History 3 Commits
EBR - an algo for SR.pdf		EBR - an algo for SR.pdf
EQL_DDP.ipynb		EQL_DDP.ipynb
EQL_DIV_Automated.ipynb		EQL_DIV_Automated.ipynb
Final_Presentation.pdf		Final_Presentation.pdf
Martius and Lampert New.pdf		Martius and Lampert New.pdf
Optimum_window_length_for_SG_Method.pdf		Optimum_window_length_for_SG_Method.pdf
README.md		README.md
Report_CH16B119.pdf		Report_CH16B119.pdf
extrapolation_and_learning_equations.pdf		extrapolation_and_learning_equations.pdf

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The focus of function approximation problems has been on identifying some suitable function without attempting to gain insight into the mechanism of the system. The performance of the model boils down to interpolation. But, in a more realistic setting, we expect test data from outside the distribution of the training set. To better extrapolate to unseen domains, it is essential to learn the correct underlying equations of the system. The Equation Learner (EQL) Network attempts to achieve this task.