CIANNA is a general-purpose deep learning framework primarily developed and used for astronomical data analysis. Functionalities and optimizations are added based on relevance for astrophysical ...

The model equations are as follows. $$ \begin{align*} \dfrac{\mathrm dS}{\mathrm dt} &= -\frac{\beta c S I}{N}, \\ \dfrac{\mathrm dI}{\mathrm dt} &= \frac{\beta c S I ...

Data tend to live in low-dimensional nonlinear subspaces. The framework of Riemannian geometry can take such nonlinearity into account: If set up properly, data seem to lie on low-dimensional linear ...

Physics-Informed Neural Networks (PINN) emerged as a powerful tool for solving scientific computing problems, ranging from the solution of Partial Differential Equations to data assimilation tasks.

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 ...

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