Grid.inverse_permeability array of shape (grid.Nx, grid.Ny, grid.Nz, 3). Reasons) to their inverses grid.inverse_permittivity array and a Internally, these variables will be converted (for performance Permittivity for each of the major axes (so-called uniaxial or biaxial In the last case, the shape implies the possibility for different Recommended minimum grid_spacing turns out to be 50pmįor the permittivity and permeability floats or arrays with the followingĪre expected. Wavelength 1550nm and a material with refractive index of 3.1, the This means that for a grid containing a source with For stability reasons, it is recommended toĬhoose a grid spacing that is at least 10 times smaller than the smallest Internally, these numbers will be translated to three integers:Ī grid_spacing can be given. If the shape is given in integers, itĭenotes the width, height and length of the grid in terms of the If the shape is given in floats, it denotes the width, Grid ( shape : Tuple, grid_spacing : float = 155e-9, permittivity : float = 1.0, permeability : float = 1.0, courant_number : float = None, )Ī grid is defined by its shape, which is just a 3D tuple of Number-types The FDTD grid defines the simulation region. The 'cuda' backends are only available for computers with a GPU. Preferred over 'float32' for FDTD simulations, however, 'float32' might In general, 'float64' precision is always In general, the 'numpy' backend is preferred for standard CPU calculations 'torch.cuda' (defaults to float64 tensors)įor example, this is how to choose the 'torch' backend: fdtd.

The 'numpy' backend is theĭefault one, but there are also several additional PyTorch backends: The fdtd library allows to choose a backend. The fdtd library is simply imported as follows: import fdtd Setting the backend The development version can be installed by cloning the repository git clone Īnd linking it with pip pip install -e fdtdĭevelopment dependencies can be installed with pip install -e fdtdĪll improvements or additions (for example new objects, sources or detectors) are The fdtd-library can be installed with pip: pip install fdtd

The FDTD simulator hasĪn optional PyTorch backend, enabling FDTD simulations on a GPU. A 3D electromagnetic FDTD simulator written in Python.

Fdtd matlab code