We introduce an extension of the hopping method, typically used in quantum systems, to mechanical networks for constructing dynamical matrices. This innovative and efficient approach facilitates the ...

This set of tutorials are written at an introductory level for an engineering or physical sciences major. It is ideal for someone who has completed college level courses in linear algebra, calculus ...

Clustering is usually the first exploratory analysis step in empirical data. When the data set comprises graphs, the most common approaches focus on clustering its vertices. In this work, we are ...

Physics-Informed Neural Networks (PINN) are neural networks encoding the problem governing equations, such as Partial Differential Equations (PDE), as a part of the neural network. PINNs have emerged ...

Dr. James McCaffrey of Microsoft Research demonstrates applying the L-BFGS optimization algorithm to the ML logistic regression technique for binary classification -- predicting one of two possible ...

Uncertainpy is a python toolbox for uncertainty quantification and sensitivity analysis tailored towards computational neuroscience. Uncertainpy is model independent and treats the model as a black ...