spgrid_topo_opt | Band Topology Optimization on a Sparsely Populated Grid

by yuanming-hu C++ Version: Current License: No License

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kandi X-RAY | spgrid_topo_opt Summary

spgrid_topo_opt is a C++ library. spgrid_topo_opt has no bugs, it has no vulnerabilities and it has low support. You can download it from GitHub.

Narrow-Band Topology Optimization on a Sparsely Populated Grid, ACM Transactions on Graphics (SIGGRAPH Asia 2018)

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Quality

Security

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Support

spgrid_topo_opt has a low active ecosystem.

It has 135 star(s) with 28 fork(s). There are 8 watchers for this library.

It had no major release in the last 6 months.

There are 5 open issues and 6 have been closed. On average issues are closed in 141 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of spgrid_topo_opt is current.

Quality

spgrid_topo_opt has no bugs reported.

Security

spgrid_topo_opt has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

spgrid_topo_opt does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

spgrid_topo_opt releases are not available. You will need to build from source code and install.

Installation instructions, examples and code snippets are available.

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spgrid_topo_opt Key Features

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spgrid_topo_opt Examples and Code Snippets

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Community Discussions

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Vulnerabilities

No vulnerabilities reported

Install spgrid_topo_opt

TC_MKL_PATH is for libmkl_rt.so.
Install taichi (legacy branch) first and put this in the projects folder.
Build the FEM solver: cd solver && make (Note: this needs Intel icc and mkl. Please install if you don't have it.)
Set the following environment variables according to your machine. For example,
ti build in shell and wait for the build to finish.
Pick a script under the scripts folder and run with python3. Some scripts have extremely high resolution (see Table 2 in our paper). If you run out of memory, you may want to use a smaller n.