TorchGDM

https://homepages.laas.fr/pwiecha/torchgdm_doc/

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TorchGDM is a PyTorch-based nano-optics full-field solver that implements the Green’s dyadic method...

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### Install TorchGDM via pip

Source: https://homepages.laas.fr/pwiecha/torchgdm_doc

Install the TorchGDM package using pip. This is the primary method for obtaining the library.

```bash
$ pip install torchgdm

```

--------------------------------

### Basic TorchGDM Simulation Setup and Execution

Source: https://homepages.laas.fr/pwiecha/torchgdm_doc

This snippet demonstrates setting up a simulation with a homogeneous environment, plane wave illumination, and a discretized structure. It includes running the simulation and plotting cross-section results.

```python
import torch
import matplotlib.pyplot as plt
import torchgdm as tg

# --- simulation setup
# - vacuum environment
mat_env = tg.materials.MatConstant(eps=1.0)
env = tg.env.EnvHomogeneous3D(env_material=mat_env)

# - illumination field(s) and wavelength
wavelengths = torch.linspace(550.0, 950.0, 25)
plane_wave = tg.env.freespace_3d.PlaneWave(e0p=1.0, e0s=0.0)

# - discretized structure
structure = tg.struct3d.StructDiscretizedCubic3D(
    discretization_config=tg.struct3d.cube(l=8),
    step=30.0,    # in nm
    materials=tg.materials.MatDatabase("GaN"),
)

# - define and run simulation.
sim = tg.Simulation(
    structures=[structure],
    illumination_fields=[plane_wave],
    environment=env,
    wavelengths=wavelengths,
)
sim.plot_structure()  # visualize structure
sim.run()             # run the main simulation

# - post-processing: cross sections
cs_results = sim.get_spectra_crosssections()

# plot
plt.figure(figsize=(5, 4))
plt.plot(tg.to_np(wavelengths), tg.to_np(cs_results["scs"]))
plt.xlabel("wavelength (nm)")
plt.ylabel("scs (nm^2)")
plt.show()

```

--------------------------------

### Enable GPU Usage in TorchGDM

Source: https://homepages.laas.fr/pwiecha/torchgdm_doc

This snippet shows how to enable GPU acceleration for TorchGDM simulations. It can be done globally or by setting the device for a specific simulation instance.

```python
import torchgdm as tg
tg.use_cuda(True)

```

```python
sim = tg.Simulation(...)
sim.set_device("cuda")

```

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