Install | Tutorials | Documentation | Changelog

j-Wave is a library of simulators for acoustic applications. Is heavily inspired by k-Wave (a big portion of j-Wave is a port of k-Wave in JAX), and its intented to be used as a collection of modular blocks that can be easily included into any machine learning pipeline.

Following the phylosophy of JAX, j-Wave is developed with the following principles in mind

1. To be differntiable
2. To be fast via jit compilation
3. Easy to run on GPUs
4. Easy to customize

Follow the instructions to install Jax with CUDA support if you want to use your GPU.

Then, simply install jwave using pip

pip install git+https://github.com/ucl-bug/jwave.git

For more details, see the Linux install guide.

Because JAX has limited support on Windows, j-Wave can be run on Windows machines only using the Windows Subsystem for Linux. See the Install on Windows guide for more details.

This example simulates an acoustic initial value problem, which is often used as a simple model for photoacoustic acquisitions:

from jax import jit

from jwave import FourierSeries
from jwave.acoustics.time_varying import simulate_wave_propagation
from jwave.geometry import Domain, Medium, TimeAxis
from jwave.utils import load_image_to_numpy

# Simulation parameters
N, dx = (256, 256), (0.1e-3, 0.1e-3)
domain = Domain(N, dx)
medium = Medium(domain=domain, sound_speed=1500.)
time_axis = TimeAxis.from_medium(medium, cfl=0.3, t_end=.8e-05)

# Initial pressure field
p0 = load_image_to_numpy("docs/assets/images/jwave.png", image_size=N)/255.
p0 = FourierSeries(p0, domain)

# Compile and run the simulation
@jit
def solver(medium, p0):
  return simulate_wave_propagation(medium, time_axis, p0=p0)

pressure = solver(medium, p0)

If you find some problems in the code or want to propose some new features, feel free to open an issue. If you generally would like to have some guidance, discuss something, or just say hi, feel free to write a message in the Discord channel.

If you use jwave for your research, please consider citing it as:

@article{stanziola2022jwave,
    author={Stanziola, Antonio and Arridge, Simon R. and Cox, Ben T. and Treeby, Bradley E.},
    title = {j-Wave: An open-source differentiable wave simulator},
    publisher = {arXiv},
    year = {2022},
}

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