🌐 BiFIS

Signed Distance Function-biased flow importance sampling for implicit neural compression of flow fields
Omar A. Mures, Miguel Cid Montoya

Usage

Installation

The bifis library is available using pip. We recommend using a virtual environment to avoid conflicts with other software on your machine.

pip install bifis

Example

from bifis.utils import Config, console
from bifis.sampling import Uniform, VariableDensity, BiFIS
from scipy.interpolate import griddata

# Load configuration
config = Config("config.json")

# Build toy deck
points, deck, path = build_toy_deck()

uniform = Uniform(config, args.resolution[0], args.resolution[1], np.arange(len(points)))
uniform.show(points[:, 0], points[:, 1], write=False)

variable_density = VariableDensity(config, args.resolution[0], args.resolution[1], np.arange(len(points)))
variable_density.show(points[:, 0], points[:, 1], write=False)

bifis = BiFIS(config, args.resolution[0], args.resolution[1], np.arange(len(points)), samples=points, surface=deck, surface_idx=np.arange(len(deck)))
bifis.show(points[:, 0], points[:, 1], write=False)

# Use resulting grid
interp_var_dens = griddata((points[:, 0], points[:, 1]), p_original, (variable_density.grid_x, variable_density.grid_y), method=interpolation_method)

# BiFIS uses cell ids instead of interpolation to get pixel data
fields_b_i = p_original[bifis.idx].reshape(bifis.img_shape)

📢 News

May 2025

• Initial code version published.

March 2025

• Code coming soon.

🎯 TODO

The repo is still under construction, thanks for your patience.

• Release pip package.
• Release of the sampling code.

📜 Citation

@Article{montoya2024aeroelastic,
  author = {Mures, Omar A. and Cid Montoya, Miguel},
  title = {Signed Distance Function-biased flow importance sampling for implicit neural compression of flow fields},
  journal = {Computer-Aided Civil and Infrastructure Engineering},
  volume = {n/a},
  number = {n/a},
  pages = {},
  doi = {n/a},
  url = {n/a},
}