Skip to main content

Library for high-dimensional PDEs using sparse grids and discontinuous Galerkin method

Project description

The ASGarD project has the goal of building a solver specifically targeting high-dimensional PDEs where the "curse-of-dimensionality" has previously precluded useful continuum / Eularian (grid or mesh based as opposed to Monte-Carlo sampling) simulation. Our approach is based on a Discontinuous-Galerkin finite-element solver build atop an adaptive hierarchical sparse-grid (note this is different from the "combination technique" when applied to sparse-grids).

To cite the ASGarD code in your work, please use:

Papers using ASGarD:

Documentation of usage: https://project-asgard.github.io/asgard/

The developer documentation contains information about how to contribute to the ASGarD project.

Quick Install

ASGarD supports --user and venv install only.

user install: python3 -m pip install ornl-asgard==0.7.1a1 --user

venv install: python3 -m pip install ornl-asgard==0.7.1a1

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

ornl_asgard-0.7.1a1.tar.gz (289.1 kB view details)

Uploaded Source

File details

Details for the file ornl_asgard-0.7.1a1.tar.gz.

File metadata

  • Download URL: ornl_asgard-0.7.1a1.tar.gz
  • Upload date:
  • Size: 289.1 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.0.0 CPython/3.12.3

File hashes

Hashes for ornl_asgard-0.7.1a1.tar.gz
Algorithm Hash digest
SHA256 2f210e125c52012ceeab8726677a1c8b254fd8b0d2f8b2d562aa5cf0c2127991
MD5 2e8255ed6d19e091cf5c7a1d5c2cf5be
BLAKE2b-256 827cf78e826733d0f63e0c5e88f36c4a71422f60e8f46391e99c7876c70102ee

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page