rmojgani / LPINNs

To address some of the failure modes in training of physics informed neural networks, a Lagrangian architecture is designed to conform to the direction of travel of information in convection-diffusion equations, i.e., method of characteristic; The repository includes a pytorch implementation of PINN and proposed LPINN with periodic boundary conditions

Date Created 2022-05-04 (2 years ago)

Commits 22 (last one about a year ago)

Stargazers 42 (0 this week)

Watchers 5 (0 this week)

Forks 5

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RepositoryStats indexes 584,777 repositories, of these rmojgani/LPINNs is ranked #502,884 (14th percentile) for total stargazers, and #332,458 for total watchers. Github reports the primary language for this repository as Python, for repositories using this language it is ranked #97,243/116,437.

rmojgani/LPINNs is also tagged with popular topics, for these it's ranked: deep-learning (#7,519/8398), pytorch (#5,293/5951), neural-network (#1,016/1092), ml (#520/603)

All Topics

ml nn pinn burgers pytorch convection lagrangian deep-learning neural-network convection-diffusion method-of-characteristics physics-informed-neural-networks

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22 commits on the default branch (main) since jan '22

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updated: 2024-09-12 @ 06:22am, id: 488672058 / R_kgDOHSCLOg